diff --git a/.gitattributes b/.gitattributes
index 44e74076f0484d91f3633b0a16d8328668421f20..1e1741421895c5464de43506a27677602782ddfc 100644
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diff --git a/OVERFIT_TROUBLESHOOTING.md b/OVERFIT_TROUBLESHOOTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..3faad719e2d7b80fdd734f401da1c6a9fb79e1d0
--- /dev/null
+++ b/OVERFIT_TROUBLESHOOTING.md
@@ -0,0 +1,115 @@
+# Overfit 排查清单（从数据 → 训练 → 推理）
+
+下面按“最可能导致结果糊”的问题优先级排序。每一条都附带定位方式和建议修复思路。
+
+## 1. 数据与标注是否真的被模型用上
+
+### 1.1 文本 prompt 在训练与推理不一致（高优先级）
+- 现状：
+  - 训练时 `pre_shot_caption` 被固定为 `["xxx", "xxx", "xxx"]`
+  - 推理时 `overfit_infer_debug.py` 会读取 `dataset.json` 的 `text` 作为 prompt
+- 影响：模型训练时从未见过真实文本，推理时给真实文本会导致条件不匹配，输出容易发散或模糊。
+- 定位：
+  - `multi-shot/multi_view/datasets/videodataset.py` 的 `__getitem__` 硬编码 `pre_shot_caption`
+- 建议：
+  - 训练时用真实 `text`（或按 shot 拆分后的文本）替换 `pre_shot_caption`
+  - 或推理时也用 `"xxx"` 保持一致（不推荐）
+
+### 1.2 overfit 推理只跑了 test split（高优先级）
+- 现状：
+  - `overfit_infer_debug.py` 里 `training=False`，Dataset 只使用 `data_test`
+  - 你的日志里 `Dataset size: 1`，说明只在 test 上推理
+- 影响：过拟合训练是在 `data_train` 上，推理却在 test 上，必然看不到“过拟合效果”
+- 定位：
+  - `MulltiShot_MultiView_Dataset(..., training=False)`
+- 建议：
+  - overfit 验证时改为 `training=True` 或直接读取全部数据（不做 train/test split）
+
+### 1.3 每次取随机 5 秒片段（高优先级）
+- 现状：
+  - `load_video_crop_ref_image` 每次随机切 5 秒，训练/推理都不确定
+- 影响：同一个 index 每次取到不同片段，过拟合目标不稳定 → 输出糊
+- 建议：
+  - overfit 模式下固定切片（比如固定从第 0 帧开始）
+  - 或设置固定随机种子，并在推理里复用同一个切片
+
+## 2. 数据预处理是否正确（process_data）
+
+### 2.1 检查处理后的视频尺寸/帧率
+```bash
+python - <<'PY'
+import json, imageio
+from pathlib import Path
+
+path = Path('/data/rczhang/PencilFolder/multi-shot/processed/dataset.json')
+data = json.load(path.open())
+first = next(iter(data.values()))
+video = first['disk_path']
+reader = imageio.get_reader(video)
+meta = reader.get_meta_data()
+frame = reader.get_data(0)
+reader.close()
+print('video:', video)
+print('fps:', meta.get('fps'))
+print('shape:', frame.shape)  # 期望 (480, 832, 3)
+PY
+```
+- 期望：fps=16，shape=(480, 832, 3)
+
+### 2.2 检查 ID 图是否存在、数量够不够
+```bash
+python - <<'PY'
+import json, os
+from pathlib import Path
+
+path = Path('/data/rczhang/PencilFolder/multi-shot/processed/dataset.json')
+data = json.load(path.open())
+first = next(iter(data.values()))
+id_dir = Path(first['id_dir'])
+print('id_dir exists:', id_dir.exists())
+print('id_names:', first.get('id_names'))
+for name in first.get('id_names', []):
+    imgs = list((id_dir / name).glob('*'))
+    print(name, 'num_imgs:', len(imgs))
+PY
+```
+
+## 3. 训练配置是否支持“过拟合检查”
+
+### 3.1 训练是否在用 ref_images
+- `train_args.zero_face_ratio`=0.1 会随机把 ref 清空 10% 步。
+- 过拟合排查建议：临时设为 `0.0`，确保 ID 图一直生效。
+
+### 3.2 训练是否从旧 checkpoint 继续
+- `resume_from_checkpoint: True`
+- 如果之前的数据或 prompt 变了，旧 ckpt 会污染。
+- 建议：排查时用新的输出目录或手动关闭 resume。
+
+## 4. 推理配置是否合理
+
+### 4.1 纯 prompt 推理 vs v2v
+- 你现在默认是纯 prompt（`input_video=None`），这是合理的。
+- 如果要确认模型是否“记住”训练样本，可临时开 v2v：
+  - `--use_input_video`
+  - 这只用于 sanity check，不是最终推理形态。
+
+### 4.2 采样步数过少
+- `num_inference_steps=8` 非常少，容易糊。
+- 排查建议：先用 20~50 步看看清晰度提升。
+
+## 5. 模型与权重是否正确
+
+### 5.1 确认加载的是你训练出的最新 checkpoint
+- `overfit_infer_debug.py` 默认取最新 `checkpoint-step-*`
+- 建议手动指定 `--checkpoint_path` 再确认一次。
+
+### 5.2 只训练了 DiT
+- `trainable_models: dit`，text encoder 和 VAE 都冻结
+- 对于过拟合一般没问题，但如果 prompt 变化很大，text encoder 冻结会让文本条件效果变弱。
+
+## 快速结论（最可能导致“全是糊图”的 3 个点）
+1) **训练/推理 prompt 不一致（训练固定 "xxx"，推理用真实 text）**  
+2) **overfit 推理用的是 test split，不是训练集**  
+3) **每次都随机 5 秒片段，过拟合目标不稳定**  
+
+如果你愿意，我可以按这个顺序逐一修掉并验证。
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+================================================================================
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_98/debug.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_98/debug.log
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@@ -0,0 +1,5 @@
+================================================================================
+[debug_infer] epoch=98 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_98/ref_0/id0_img0.png b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_98/ref_0/id0_img0.png
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_99/debug.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_99/debug.log
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+================================================================================
+[debug_infer] epoch=99 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_99/ref_0/id0_img0.png b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/epoch_99/ref_0/id0_img0.png
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 263067
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+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer
+Checkpoint: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/final_model/model.safetensors
+Dataset split: all
+Dataset size: 5
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer
+Checkpoint: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/final_model/model.safetensors
+Dataset split: all
+Dataset size: 5
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer
+Checkpoint: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/converted_weights.safetensors
+Dataset split: all
+Dataset size: 5
+================================================================================
+[Sample 0]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data1/1.mp4
+text: 画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄
+shot_caption: ['画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/ref_images_0
+[GPU] before_infer allocated=17.94GB reserved=21.24GB
+[GPU] after_infer allocated=0.04GB reserved=0.09GB
+inference_time: 89.51s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/output_0.mp4
+Done.
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer
+Checkpoint: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/converted_weights.safetensors
+Dataset split: all
+Dataset size: 5
+================================================================================
+[Sample 0]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data1/1.mp4
+text: 画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄
+shot_caption: ['画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/ref_images_0
+[GPU] before_infer allocated=17.94GB reserved=21.24GB
+[GPU] after_infer allocated=0.04GB reserved=0.09GB
+inference_time: 91.03s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/output_0.mp4
+================================================================================
+[Sample 1]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+text: 画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。 画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄
+shot_caption: ['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。 画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/ref_images_1
+[GPU] before_infer allocated=0.04GB reserved=0.09GB
+[GPU] after_infer allocated=0.04GB reserved=0.81GB
+inference_time: 82.50s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/output_1.mp4
+================================================================================
+[Sample 2]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data2/1.mp4
+text: 画面右侧的白人中年男性面向镜头，头部微微向左转动，眼睛注视着画面左侧的人物。他的表情严肃，嘴唇紧闭，显示出专注和思考的状态。他身穿深色衣物，肩部有复杂的纹理装饰。背景是一个昏暗的室内环境，光线柔和而集中，营造出一种紧张的氛围。该视频以静止运镜手法在特写上半身的镜头下展现。 画面右侧一位白人中年男性，他有着长长的银白色头发，身穿深色的盔甲。他的表情平静，目光专注地望向镜头，双手放在桌面上，手指轻轻触碰。画面左侧有一个人背对着镜头，没有拍摄到这个人的头部。背景是在一个昏暗的室内环境中，几支点燃的蜡烛散发着柔和的光芒，营造出一种温暖而神秘的氛围。该视频以晃动的手法，在特写人物上半身的画面下展示。
+shot_caption: ['画面右侧的白人中年男性面向镜头，头部微微向左转动，眼睛注视着画面左侧的人物。他的表情严肃，嘴唇紧闭，显示出专注和思考的状态。他身穿深色衣物，肩部有复杂的纹理装饰。背景是一个昏暗的室内环境，光线柔和而集中，营造出一种紧张的氛围。该视频以静止运镜手法在特写上半身的镜头下展现。 画面右侧一位白人中年男性，他有着长长的银白色头发，身穿深色的盔甲。他的表情平静，目光专注地望向镜头，双手放在桌面上，手指轻轻触碰。画面左侧有一个人背对着镜头，没有拍摄到这个人的头部。背景是在一个昏暗的室内环境中，几支点燃的蜡烛散发着柔和的光芒，营造出一种温暖而神秘的氛围。该视频以晃动的手法，在特写人物上半身的画面下展示。']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/ref_images_2
+[GPU] before_infer allocated=0.04GB reserved=0.81GB
+[GPU] after_infer allocated=0.04GB reserved=0.83GB
+inference_time: 84.26s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/output_2.mp4
+================================================================================
+[Sample 3]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data2/2.mp4
+text: 画面中间偏左的白人青年女性面向镜头，嘴巴微微张开，眼睛眨动着，身体和头部轻微晃动。她穿着黑色衣服，戴着金色项链和耳环。背景中可以看到一扇木质门和一些家具轮廓。整个场景营造出一种静谧而紧张的氛围。该视频以推同时晃动的运镜手法，在特写上半身的画面下展示。
+shot_caption: ['画面中间偏左的白人青年女性面向镜头，嘴巴微微张开，眼睛眨动着，身体和头部轻微晃动。她穿着黑色衣服，戴着金色项链和耳环。背景中可以看到一扇木质门和一些家具轮廓。整个场景营造出一种静谧而紧张的氛围。该视频以推同时晃动的运镜手法，在特写上半身的画面下展示。']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/ref_images_3
+[GPU] before_infer allocated=0.04GB reserved=0.83GB
+[GPU] after_infer allocated=0.04GB reserved=1.14GB
+inference_time: 82.86s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/debug_infer/output_3.mp4
+Done.
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+[2026-01-09 18:31:21] Logger initialized. Current global_step: 0
+[2026-01-09 18:31:32] Epoch: 0 | Iter: 000000 | Step Time: 4.383s | Local Loss: 0.0259 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-09 18:31:36] Epoch: 0 | Iter: 000001 | Step Time: 3.537s | Local Loss: 0.1866 | Avg Loss: 0.1866 | LR: 0.000010
+[2026-01-09 18:31:39] Epoch: 0 | Iter: 000002 | Step Time: 3.576s | Local Loss: 0.2627 | Avg Loss: 0.2627 | LR: 0.000010
+[2026-01-09 18:31:43] Epoch: 0 | Iter: 000003 | Step Time: 3.601s | Local Loss: 0.5280 | Avg Loss: 0.5280 | LR: 0.000010
+[2026-01-09 18:31:49] Epoch: 1 | Iter: 000004 | Step Time: 3.830s | Local Loss: 0.1528 | Avg Loss: 0.1528 | LR: 0.000010
+[2026-01-09 18:31:53] Epoch: 1 | Iter: 000005 | Step Time: 3.590s | Local Loss: 0.6157 | Avg Loss: 0.6157 | LR: 0.000010
+[2026-01-09 18:31:57] Epoch: 1 | Iter: 000006 | Step Time: 3.610s | Local Loss: 0.0644 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-09 18:32:00] Epoch: 1 | Iter: 000007 | Step Time: 3.576s | Local Loss: 0.2423 | Avg Loss: 0.2423 | LR: 0.000010
+[2026-01-09 18:32:06] Epoch: 2 | Iter: 000008 | Step Time: 3.610s | Local Loss: 0.3554 | Avg Loss: 0.3554 | LR: 0.000010
+[2026-01-09 18:32:10] Epoch: 2 | Iter: 000009 | Step Time: 3.570s | Local Loss: 0.4738 | Avg Loss: 0.4738 | LR: 0.000010
+[2026-01-09 18:32:14] Epoch: 2 | Iter: 000010 | Step Time: 3.592s | Local Loss: 0.0277 | Avg Loss: 0.0277 | LR: 0.000010
+[2026-01-09 18:32:17] Epoch: 2 | Iter: 000011 | Step Time: 3.628s | Local Loss: 0.0494 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-09 18:32:24] Epoch: 3 | Iter: 000012 | Step Time: 3.677s | Local Loss: 0.0920 | Avg Loss: 0.0920 | LR: 0.000010
+[2026-01-09 18:32:27] Epoch: 3 | Iter: 000013 | Step Time: 3.554s | Local Loss: 0.0688 | Avg Loss: 0.0688 | LR: 0.000010
+[2026-01-09 18:32:31] Epoch: 3 | Iter: 000014 | Step Time: 3.762s | Local Loss: 0.1549 | Avg Loss: 0.1549 | LR: 0.000010
+[2026-01-09 18:32:35] Epoch: 3 | Iter: 000015 | Step Time: 3.572s | Local Loss: 0.5011 | Avg Loss: 0.5011 | LR: 0.000010
+[2026-01-09 18:32:41] Epoch: 4 | Iter: 000016 | Step Time: 3.785s | Local Loss: 0.3290 | Avg Loss: 0.3290 | LR: 0.000010
+[2026-01-09 18:32:44] Epoch: 4 | Iter: 000017 | Step Time: 3.551s | Local Loss: 0.1185 | Avg Loss: 0.1185 | LR: 0.000010
+[2026-01-09 18:32:48] Epoch: 4 | Iter: 000018 | Step Time: 3.557s | Local Loss: 0.2712 | Avg Loss: 0.2712 | LR: 0.000010
+[2026-01-09 18:32:52] Epoch: 4 | Iter: 000019 | Step Time: 3.602s | Local Loss: 0.0198 | Avg Loss: 0.0198 | LR: 0.000010
+[2026-01-09 18:32:58] Epoch: 5 | Iter: 000020 | Step Time: 3.617s | Local Loss: 0.3215 | Avg Loss: 0.3215 | LR: 0.000010
+[2026-01-09 18:33:02] Epoch: 5 | Iter: 000021 | Step Time: 3.539s | Local Loss: 0.0546 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-09 18:33:05] Epoch: 5 | Iter: 000022 | Step Time: 3.513s | Local Loss: 0.1449 | Avg Loss: 0.1449 | LR: 0.000010
+[2026-01-09 18:33:09] Epoch: 5 | Iter: 000023 | Step Time: 3.560s | Local Loss: 0.0871 | Avg Loss: 0.0871 | LR: 0.000010
+[2026-01-09 18:33:15] Epoch: 6 | Iter: 000024 | Step Time: 3.553s | Local Loss: 0.1089 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-09 18:33:18] Epoch: 6 | Iter: 000025 | Step Time: 3.641s | Local Loss: 0.3366 | Avg Loss: 0.3366 | LR: 0.000010
+[2026-01-09 18:33:22] Epoch: 6 | Iter: 000026 | Step Time: 3.556s | Local Loss: 0.0661 | Avg Loss: 0.0661 | LR: 0.000010
+[2026-01-09 18:33:26] Epoch: 6 | Iter: 000027 | Step Time: 3.624s | Local Loss: 0.1511 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-09 18:33:32] Epoch: 7 | Iter: 000028 | Step Time: 3.600s | Local Loss: 0.0776 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-09 18:33:36] Epoch: 7 | Iter: 000029 | Step Time: 3.600s | Local Loss: 0.2663 | Avg Loss: 0.2663 | LR: 0.000010
+[2026-01-09 18:33:40] Epoch: 7 | Iter: 000030 | Step Time: 3.610s | Local Loss: 0.1657 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-09 18:33:43] Epoch: 7 | Iter: 000031 | Step Time: 3.561s | Local Loss: 0.1898 | Avg Loss: 0.1898 | LR: 0.000010
+[2026-01-09 18:33:49] Epoch: 8 | Iter: 000032 | Step Time: 3.600s | Local Loss: 0.0200 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-09 18:33:53] Epoch: 8 | Iter: 000033 | Step Time: 3.556s | Local Loss: 0.1425 | Avg Loss: 0.1425 | LR: 0.000010
+[2026-01-09 18:33:56] Epoch: 8 | Iter: 000034 | Step Time: 3.569s | Local Loss: 0.0513 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-09 18:34:00] Epoch: 8 | Iter: 000035 | Step Time: 3.546s | Local Loss: 0.1992 | Avg Loss: 0.1992 | LR: 0.000010
+[2026-01-09 18:34:06] Epoch: 9 | Iter: 000036 | Step Time: 3.904s | Local Loss: 0.1577 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-09 18:34:10] Epoch: 9 | Iter: 000037 | Step Time: 3.560s | Local Loss: 0.1687 | Avg Loss: 0.1687 | LR: 0.000010
+[2026-01-09 18:34:14] Epoch: 9 | Iter: 000038 | Step Time: 3.536s | Local Loss: 0.2186 | Avg Loss: 0.2186 | LR: 0.000010
+[2026-01-09 18:34:17] Epoch: 9 | Iter: 000039 | Step Time: 3.569s | Local Loss: 0.1373 | Avg Loss: 0.1373 | LR: 0.000010
+[2026-01-09 18:35:11] Logger closing.
+[2026-01-09 18:58:45] Logger initialized. Current global_step: 0
+[2026-01-09 18:58:58] Epoch: 0 | Iter: 000000 | Step Time: 4.511s | Local Loss: 0.0259 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-09 18:59:02] Epoch: 0 | Iter: 000001 | Step Time: 3.550s | Local Loss: 0.1866 | Avg Loss: 0.1866 | LR: 0.000010
+[2026-01-09 18:59:05] Epoch: 0 | Iter: 000002 | Step Time: 3.558s | Local Loss: 0.2627 | Avg Loss: 0.2627 | LR: 0.000010
+[2026-01-09 18:59:09] Epoch: 0 | Iter: 000003 | Step Time: 3.574s | Local Loss: 0.5280 | Avg Loss: 0.5280 | LR: 0.000010
+[2026-01-09 18:59:15] Epoch: 1 | Iter: 000004 | Step Time: 3.617s | Local Loss: 0.1528 | Avg Loss: 0.1528 | LR: 0.000010
+[2026-01-09 18:59:19] Epoch: 1 | Iter: 000005 | Step Time: 3.605s | Local Loss: 0.6158 | Avg Loss: 0.6158 | LR: 0.000010
+[2026-01-09 18:59:23] Epoch: 1 | Iter: 000006 | Step Time: 3.556s | Local Loss: 0.0644 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-09 18:59:26] Epoch: 1 | Iter: 000007 | Step Time: 3.532s | Local Loss: 0.2423 | Avg Loss: 0.2423 | LR: 0.000010
+[2026-01-09 18:59:32] Epoch: 2 | Iter: 000008 | Step Time: 3.575s | Local Loss: 0.3554 | Avg Loss: 0.3554 | LR: 0.000010
+[2026-01-09 18:59:36] Epoch: 2 | Iter: 000009 | Step Time: 3.517s | Local Loss: 0.4738 | Avg Loss: 0.4738 | LR: 0.000010
+[2026-01-09 18:59:40] Epoch: 2 | Iter: 000010 | Step Time: 3.565s | Local Loss: 0.0277 | Avg Loss: 0.0277 | LR: 0.000010
+[2026-01-09 18:59:43] Epoch: 2 | Iter: 000011 | Step Time: 3.566s | Local Loss: 0.0494 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-09 18:59:50] Epoch: 3 | Iter: 000012 | Step Time: 3.610s | Local Loss: 0.0920 | Avg Loss: 0.0920 | LR: 0.000010
+[2026-01-09 18:59:53] Epoch: 3 | Iter: 000013 | Step Time: 3.524s | Local Loss: 0.0687 | Avg Loss: 0.0687 | LR: 0.000010
+[2026-01-09 18:59:57] Epoch: 3 | Iter: 000014 | Step Time: 3.607s | Local Loss: 0.1549 | Avg Loss: 0.1549 | LR: 0.000010
+[2026-01-09 19:00:01] Epoch: 3 | Iter: 000015 | Step Time: 3.622s | Local Loss: 0.5011 | Avg Loss: 0.5011 | LR: 0.000010
+[2026-01-09 19:00:07] Epoch: 4 | Iter: 000016 | Step Time: 3.789s | Local Loss: 0.3290 | Avg Loss: 0.3290 | LR: 0.000010
+[2026-01-09 19:00:10] Epoch: 4 | Iter: 000017 | Step Time: 3.573s | Local Loss: 0.1184 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-09 19:00:14] Epoch: 4 | Iter: 000018 | Step Time: 3.533s | Local Loss: 0.2712 | Avg Loss: 0.2712 | LR: 0.000010
+[2026-01-09 19:00:18] Epoch: 4 | Iter: 000019 | Step Time: 3.569s | Local Loss: 0.0197 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-09 19:00:24] Epoch: 5 | Iter: 000020 | Step Time: 3.880s | Local Loss: 0.3215 | Avg Loss: 0.3215 | LR: 0.000010
+[2026-01-09 19:00:28] Epoch: 5 | Iter: 000021 | Step Time: 3.544s | Local Loss: 0.0547 | Avg Loss: 0.0547 | LR: 0.000010
+[2026-01-09 19:00:31] Epoch: 5 | Iter: 000022 | Step Time: 3.541s | Local Loss: 0.1451 | Avg Loss: 0.1451 | LR: 0.000010
+[2026-01-09 19:00:35] Epoch: 5 | Iter: 000023 | Step Time: 3.570s | Local Loss: 0.0871 | Avg Loss: 0.0871 | LR: 0.000010
+[2026-01-09 19:00:41] Epoch: 6 | Iter: 000024 | Step Time: 3.555s | Local Loss: 0.1089 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-09 19:00:44] Epoch: 6 | Iter: 000025 | Step Time: 3.562s | Local Loss: 0.3365 | Avg Loss: 0.3365 | LR: 0.000010
+[2026-01-09 19:00:48] Epoch: 6 | Iter: 000026 | Step Time: 3.587s | Local Loss: 0.0661 | Avg Loss: 0.0661 | LR: 0.000010
+[2026-01-09 19:00:52] Epoch: 6 | Iter: 000027 | Step Time: 3.617s | Local Loss: 0.1509 | Avg Loss: 0.1509 | LR: 0.000010
+[2026-01-09 19:00:59] Epoch: 7 | Iter: 000028 | Step Time: 3.924s | Local Loss: 0.0777 | Avg Loss: 0.0777 | LR: 0.000010
+[2026-01-09 19:01:02] Epoch: 7 | Iter: 000029 | Step Time: 3.506s | Local Loss: 0.2661 | Avg Loss: 0.2661 | LR: 0.000010
+[2026-01-09 19:01:06] Epoch: 7 | Iter: 000030 | Step Time: 3.601s | Local Loss: 0.1655 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-09 19:01:10] Epoch: 7 | Iter: 000031 | Step Time: 3.570s | Local Loss: 0.1898 | Avg Loss: 0.1898 | LR: 0.000010
+[2026-01-09 19:01:16] Epoch: 8 | Iter: 000032 | Step Time: 3.577s | Local Loss: 0.0200 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-09 19:01:19] Epoch: 8 | Iter: 000033 | Step Time: 3.535s | Local Loss: 0.1424 | Avg Loss: 0.1424 | LR: 0.000010
+[2026-01-09 19:01:23] Epoch: 8 | Iter: 000034 | Step Time: 3.619s | Local Loss: 0.0514 | Avg Loss: 0.0514 | LR: 0.000010
+[2026-01-09 19:01:26] Epoch: 8 | Iter: 000035 | Step Time: 3.511s | Local Loss: 0.1994 | Avg Loss: 0.1994 | LR: 0.000010
+[2026-01-09 19:01:32] Epoch: 9 | Iter: 000036 | Step Time: 3.669s | Local Loss: 0.1576 | Avg Loss: 0.1576 | LR: 0.000010
+[2026-01-09 19:01:36] Epoch: 9 | Iter: 000037 | Step Time: 3.576s | Local Loss: 0.1686 | Avg Loss: 0.1686 | LR: 0.000010
+[2026-01-09 19:01:40] Epoch: 9 | Iter: 000038 | Step Time: 3.543s | Local Loss: 0.2186 | Avg Loss: 0.2186 | LR: 0.000010
+[2026-01-09 19:01:43] Epoch: 9 | Iter: 000039 | Step Time: 3.617s | Local Loss: 0.1372 | Avg Loss: 0.1372 | LR: 0.000010
+[2026-01-09 19:01:50] Epoch: 10 | Iter: 000040 | Step Time: 3.615s | Local Loss: 0.1790 | Avg Loss: 0.1790 | LR: 0.000010
+[2026-01-09 19:01:53] Epoch: 10 | Iter: 000041 | Step Time: 3.521s | Local Loss: 0.1872 | Avg Loss: 0.1872 | LR: 0.000010
+[2026-01-09 19:01:57] Epoch: 10 | Iter: 000042 | Step Time: 3.773s | Local Loss: 0.1115 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-09 19:02:01] Epoch: 10 | Iter: 000043 | Step Time: 3.587s | Local Loss: 0.1815 | Avg Loss: 0.1815 | LR: 0.000010
+[2026-01-09 19:02:07] Epoch: 11 | Iter: 000044 | Step Time: 3.767s | Local Loss: 0.4785 | Avg Loss: 0.4785 | LR: 0.000010
+[2026-01-09 19:02:11] Epoch: 11 | Iter: 000045 | Step Time: 3.570s | Local Loss: 0.2065 | Avg Loss: 0.2065 | LR: 0.000010
+[2026-01-09 19:02:15] Epoch: 11 | Iter: 000046 | Step Time: 3.586s | Local Loss: 0.0420 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-09 19:02:18] Epoch: 11 | Iter: 000047 | Step Time: 3.576s | Local Loss: 0.2179 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-09 19:02:24] Epoch: 12 | Iter: 000048 | Step Time: 3.553s | Local Loss: 0.0758 | Avg Loss: 0.0758 | LR: 0.000010
+[2026-01-09 19:02:28] Epoch: 12 | Iter: 000049 | Step Time: 3.578s | Local Loss: 0.2263 | Avg Loss: 0.2263 | LR: 0.000010
+[2026-01-09 19:02:32] Epoch: 12 | Iter: 000050 | Step Time: 3.577s | Local Loss: 0.1035 | Avg Loss: 0.1035 | LR: 0.000010
+[2026-01-09 19:02:35] Epoch: 12 | Iter: 000051 | Step Time: 3.562s | Local Loss: 0.0822 | Avg Loss: 0.0822 | LR: 0.000010
+[2026-01-09 19:02:41] Epoch: 13 | Iter: 000052 | Step Time: 3.608s | Local Loss: 0.5166 | Avg Loss: 0.5166 | LR: 0.000010
+[2026-01-09 19:02:45] Epoch: 13 | Iter: 000053 | Step Time: 3.571s | Local Loss: 0.1700 | Avg Loss: 0.1700 | LR: 0.000010
+[2026-01-09 19:02:49] Epoch: 13 | Iter: 000054 | Step Time: 3.538s | Local Loss: 0.2465 | Avg Loss: 0.2465 | LR: 0.000010
+[2026-01-09 19:02:52] Epoch: 13 | Iter: 000055 | Step Time: 3.747s | Local Loss: 0.5188 | Avg Loss: 0.5188 | LR: 0.000010
+[2026-01-09 19:02:59] Epoch: 14 | Iter: 000056 | Step Time: 3.778s | Local Loss: 0.3342 | Avg Loss: 0.3342 | LR: 0.000010
+[2026-01-09 19:03:02] Epoch: 14 | Iter: 000057 | Step Time: 3.556s | Local Loss: 0.1306 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-09 19:03:06] Epoch: 14 | Iter: 000058 | Step Time: 3.533s | Local Loss: 0.0721 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-09 19:03:11] Epoch: 14 | Iter: 000059 | Step Time: 4.441s | Local Loss: 0.0810 | Avg Loss: 0.0810 | LR: 0.000010
+[2026-01-09 19:03:17] Epoch: 15 | Iter: 000060 | Step Time: 3.748s | Local Loss: 0.1658 | Avg Loss: 0.1658 | LR: 0.000010
+[2026-01-09 19:03:20] Epoch: 15 | Iter: 000061 | Step Time: 3.526s | Local Loss: 0.0581 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-09 19:03:24] Epoch: 15 | Iter: 000062 | Step Time: 3.579s | Local Loss: 0.4898 | Avg Loss: 0.4898 | LR: 0.000010
+[2026-01-09 19:03:28] Epoch: 15 | Iter: 000063 | Step Time: 3.596s | Local Loss: 0.1949 | Avg Loss: 0.1949 | LR: 0.000010
+[2026-01-09 19:03:34] Epoch: 16 | Iter: 000064 | Step Time: 3.781s | Local Loss: 0.5114 | Avg Loss: 0.5114 | LR: 0.000010
+[2026-01-09 19:03:38] Epoch: 16 | Iter: 000065 | Step Time: 3.523s | Local Loss: 0.0191 | Avg Loss: 0.0191 | LR: 0.000010
+[2026-01-09 19:03:41] Epoch: 16 | Iter: 000066 | Step Time: 3.557s | Local Loss: 0.3345 | Avg Loss: 0.3345 | LR: 0.000010
+[2026-01-09 19:03:45] Epoch: 16 | Iter: 000067 | Step Time: 3.552s | Local Loss: 0.0359 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-09 19:03:51] Epoch: 17 | Iter: 000068 | Step Time: 3.644s | Local Loss: 0.0317 | Avg Loss: 0.0317 | LR: 0.000010
+[2026-01-09 19:03:55] Epoch: 17 | Iter: 000069 | Step Time: 3.591s | Local Loss: 0.1067 | Avg Loss: 0.1067 | LR: 0.000010
+[2026-01-09 19:03:58] Epoch: 17 | Iter: 000070 | Step Time: 3.530s | Local Loss: 0.1441 | Avg Loss: 0.1441 | LR: 0.000010
+[2026-01-09 19:04:02] Epoch: 17 | Iter: 000071 | Step Time: 3.552s | Local Loss: 0.0452 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-09 19:04:08] Epoch: 18 | Iter: 000072 | Step Time: 3.545s | Local Loss: 0.0539 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-09 19:04:12] Epoch: 18 | Iter: 000073 | Step Time: 3.549s | Local Loss: 0.1110 | Avg Loss: 0.1110 | LR: 0.000010
+[2026-01-09 19:04:15] Epoch: 18 | Iter: 000074 | Step Time: 3.538s | Local Loss: 0.5173 | Avg Loss: 0.5173 | LR: 0.000010
+[2026-01-09 19:04:19] Epoch: 18 | Iter: 000075 | Step Time: 3.557s | Local Loss: 0.0294 | Avg Loss: 0.0294 | LR: 0.000010
+[2026-01-09 19:04:25] Epoch: 19 | Iter: 000076 | Step Time: 3.527s | Local Loss: 0.2251 | Avg Loss: 0.2251 | LR: 0.000010
+[2026-01-09 19:04:28] Epoch: 19 | Iter: 000077 | Step Time: 3.530s | Local Loss: 0.1939 | Avg Loss: 0.1939 | LR: 0.000010
+[2026-01-09 19:04:32] Epoch: 19 | Iter: 000078 | Step Time: 3.541s | Local Loss: 0.3300 | Avg Loss: 0.3300 | LR: 0.000010
+[2026-01-09 19:04:36] Epoch: 19 | Iter: 000079 | Step Time: 3.573s | Local Loss: 0.5134 | Avg Loss: 0.5134 | LR: 0.000010
+[2026-01-09 19:04:42] Epoch: 20 | Iter: 000080 | Step Time: 3.656s | Local Loss: 0.1022 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-09 19:04:45] Epoch: 20 | Iter: 000081 | Step Time: 3.561s | Local Loss: 0.0883 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-09 19:04:49] Epoch: 20 | Iter: 000082 | Step Time: 3.591s | Local Loss: 0.0153 | Avg Loss: 0.0153 | LR: 0.000010
+[2026-01-09 19:04:53] Epoch: 20 | Iter: 000083 | Step Time: 3.543s | Local Loss: 0.0789 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-09 19:04:59] Epoch: 21 | Iter: 000084 | Step Time: 3.541s | Local Loss: 0.2507 | Avg Loss: 0.2507 | LR: 0.000010
+[2026-01-09 19:05:02] Epoch: 21 | Iter: 000085 | Step Time: 3.542s | Local Loss: 0.2170 | Avg Loss: 0.2170 | LR: 0.000010
+[2026-01-09 19:05:06] Epoch: 21 | Iter: 000086 | Step Time: 3.507s | Local Loss: 0.1860 | Avg Loss: 0.1860 | LR: 0.000010
+[2026-01-09 19:05:09] Epoch: 21 | Iter: 000087 | Step Time: 3.528s | Local Loss: 0.0712 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-09 19:05:15] Epoch: 22 | Iter: 000088 | Step Time: 3.625s | Local Loss: 0.0969 | Avg Loss: 0.0969 | LR: 0.000010
+[2026-01-09 19:05:19] Epoch: 22 | Iter: 000089 | Step Time: 3.550s | Local Loss: 0.2655 | Avg Loss: 0.2655 | LR: 0.000010
+[2026-01-09 19:05:23] Epoch: 22 | Iter: 000090 | Step Time: 3.550s | Local Loss: 0.0200 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-09 19:05:26] Epoch: 22 | Iter: 000091 | Step Time: 3.507s | Local Loss: 0.0589 | Avg Loss: 0.0589 | LR: 0.000010
+[2026-01-09 19:05:32] Epoch: 23 | Iter: 000092 | Step Time: 3.555s | Local Loss: 0.4816 | Avg Loss: 0.4816 | LR: 0.000010
+[2026-01-09 19:05:36] Epoch: 23 | Iter: 000093 | Step Time: 3.535s | Local Loss: 0.0561 | Avg Loss: 0.0561 | LR: 0.000010
+[2026-01-09 19:05:40] Epoch: 23 | Iter: 000094 | Step Time: 3.528s | Local Loss: 0.4085 | Avg Loss: 0.4085 | LR: 0.000010
+[2026-01-09 19:05:43] Epoch: 23 | Iter: 000095 | Step Time: 3.516s | Local Loss: 0.5579 | Avg Loss: 0.5579 | LR: 0.000010
+[2026-01-09 19:05:49] Epoch: 24 | Iter: 000096 | Step Time: 3.760s | Local Loss: 0.0954 | Avg Loss: 0.0954 | LR: 0.000010
+[2026-01-09 19:05:53] Epoch: 24 | Iter: 000097 | Step Time: 3.580s | Local Loss: 0.6070 | Avg Loss: 0.6070 | LR: 0.000010
+[2026-01-09 19:05:57] Epoch: 24 | Iter: 000098 | Step Time: 3.534s | Local Loss: 0.0889 | Avg Loss: 0.0889 | LR: 0.000010
+[2026-01-09 19:06:00] Epoch: 24 | Iter: 000099 | Step Time: 3.535s | Local Loss: 0.1063 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-09 19:06:06] Epoch: 25 | Iter: 000100 | Step Time: 3.570s | Local Loss: 0.2837 | Avg Loss: 0.2837 | LR: 0.000010
+[2026-01-09 19:06:10] Epoch: 25 | Iter: 000101 | Step Time: 3.512s | Local Loss: 0.0559 | Avg Loss: 0.0559 | LR: 0.000010
+[2026-01-09 19:06:13] Epoch: 25 | Iter: 000102 | Step Time: 3.538s | Local Loss: 0.1615 | Avg Loss: 0.1615 | LR: 0.000010
+[2026-01-09 19:06:17] Epoch: 25 | Iter: 000103 | Step Time: 3.534s | Local Loss: 0.3516 | Avg Loss: 0.3516 | LR: 0.000010
+[2026-01-09 19:06:23] Epoch: 26 | Iter: 000104 | Step Time: 3.859s | Local Loss: 0.0723 | Avg Loss: 0.0723 | LR: 0.000010
+[2026-01-09 19:06:27] Epoch: 26 | Iter: 000105 | Step Time: 3.544s | Local Loss: 0.4111 | Avg Loss: 0.4111 | LR: 0.000010
+[2026-01-09 19:06:30] Epoch: 26 | Iter: 000106 | Step Time: 3.556s | Local Loss: 0.1715 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-09 19:06:34] Epoch: 26 | Iter: 000107 | Step Time: 3.554s | Local Loss: 0.1225 | Avg Loss: 0.1225 | LR: 0.000010
+[2026-01-09 19:06:40] Epoch: 27 | Iter: 000108 | Step Time: 3.543s | Local Loss: 0.0197 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-09 19:06:44] Epoch: 27 | Iter: 000109 | Step Time: 3.572s | Local Loss: 0.1939 | Avg Loss: 0.1939 | LR: 0.000010
+[2026-01-09 19:06:48] Epoch: 27 | Iter: 000110 | Step Time: 3.547s | Local Loss: 0.0447 | Avg Loss: 0.0447 | LR: 0.000010
+[2026-01-09 19:06:51] Epoch: 27 | Iter: 000111 | Step Time: 3.608s | Local Loss: 0.1671 | Avg Loss: 0.1671 | LR: 0.000010
+[2026-01-09 19:06:57] Epoch: 28 | Iter: 000112 | Step Time: 3.557s | Local Loss: 0.1176 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-09 19:07:01] Epoch: 28 | Iter: 000113 | Step Time: 3.500s | Local Loss: 0.1528 | Avg Loss: 0.1528 | LR: 0.000010
+[2026-01-09 19:07:04] Epoch: 28 | Iter: 000114 | Step Time: 3.598s | Local Loss: 0.3191 | Avg Loss: 0.3191 | LR: 0.000010
+[2026-01-09 19:07:08] Epoch: 28 | Iter: 000115 | Step Time: 3.566s | Local Loss: 0.0214 | Avg Loss: 0.0214 | LR: 0.000010
+[2026-01-09 19:07:14] Epoch: 29 | Iter: 000116 | Step Time: 3.594s | Local Loss: 0.2555 | Avg Loss: 0.2555 | LR: 0.000010
+[2026-01-09 19:07:18] Epoch: 29 | Iter: 000117 | Step Time: 3.533s | Local Loss: 0.4295 | Avg Loss: 0.4295 | LR: 0.000010
+[2026-01-09 19:07:21] Epoch: 29 | Iter: 000118 | Step Time: 3.530s | Local Loss: 0.1817 | Avg Loss: 0.1817 | LR: 0.000010
+[2026-01-09 19:07:25] Epoch: 29 | Iter: 000119 | Step Time: 3.523s | Local Loss: 0.0777 | Avg Loss: 0.0777 | LR: 0.000010
+[2026-01-09 19:07:30] Epoch: 30 | Iter: 000120 | Step Time: 3.544s | Local Loss: 0.2284 | Avg Loss: 0.2284 | LR: 0.000010
+[2026-01-09 19:07:35] Epoch: 30 | Iter: 000121 | Step Time: 3.570s | Local Loss: 0.1622 | Avg Loss: 0.1622 | LR: 0.000010
+[2026-01-09 19:07:38] Epoch: 30 | Iter: 000122 | Step Time: 3.551s | Local Loss: 0.1071 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-09 19:07:42] Epoch: 30 | Iter: 000123 | Step Time: 3.580s | Local Loss: 0.0376 | Avg Loss: 0.0376 | LR: 0.000010
+[2026-01-09 19:07:48] Epoch: 31 | Iter: 000124 | Step Time: 3.642s | Local Loss: 0.2973 | Avg Loss: 0.2973 | LR: 0.000010
+[2026-01-09 19:07:51] Epoch: 31 | Iter: 000125 | Step Time: 3.551s | Local Loss: 0.2778 | Avg Loss: 0.2778 | LR: 0.000010
+[2026-01-09 19:07:55] Epoch: 31 | Iter: 000126 | Step Time: 3.522s | Local Loss: 0.3149 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-09 19:07:59] Epoch: 31 | Iter: 000127 | Step Time: 3.536s | Local Loss: 0.2029 | Avg Loss: 0.2029 | LR: 0.000010
+[2026-01-09 19:08:05] Epoch: 32 | Iter: 000128 | Step Time: 3.835s | Local Loss: 0.2500 | Avg Loss: 0.2500 | LR: 0.000010
+[2026-01-09 19:08:08] Epoch: 32 | Iter: 000129 | Step Time: 3.562s | Local Loss: 0.5626 | Avg Loss: 0.5626 | LR: 0.000010
+[2026-01-09 19:08:12] Epoch: 32 | Iter: 000130 | Step Time: 3.546s | Local Loss: 0.2921 | Avg Loss: 0.2921 | LR: 0.000010
+[2026-01-09 19:08:16] Epoch: 32 | Iter: 000131 | Step Time: 3.555s | Local Loss: 0.0338 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-09 19:08:22] Epoch: 33 | Iter: 000132 | Step Time: 3.768s | Local Loss: 0.0939 | Avg Loss: 0.0939 | LR: 0.000010
+[2026-01-09 19:08:25] Epoch: 33 | Iter: 000133 | Step Time: 3.539s | Local Loss: 0.2114 | Avg Loss: 0.2114 | LR: 0.000010
+[2026-01-09 19:08:29] Epoch: 33 | Iter: 000134 | Step Time: 3.564s | Local Loss: 0.0170 | Avg Loss: 0.0170 | LR: 0.000010
+[2026-01-09 19:08:33] Epoch: 33 | Iter: 000135 | Step Time: 3.520s | Local Loss: 0.0604 | Avg Loss: 0.0604 | LR: 0.000010
+[2026-01-09 19:08:39] Epoch: 34 | Iter: 000136 | Step Time: 3.565s | Local Loss: 0.1282 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-09 19:08:43] Epoch: 34 | Iter: 000137 | Step Time: 3.611s | Local Loss: 0.0254 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-09 19:08:46] Epoch: 34 | Iter: 000138 | Step Time: 3.531s | Local Loss: 0.1201 | Avg Loss: 0.1201 | LR: 0.000010
+[2026-01-09 19:08:50] Epoch: 34 | Iter: 000139 | Step Time: 3.576s | Local Loss: 0.1150 | Avg Loss: 0.1150 | LR: 0.000010
+[2026-01-09 19:08:56] Epoch: 35 | Iter: 000140 | Step Time: 3.902s | Local Loss: 0.0515 | Avg Loss: 0.0515 | LR: 0.000010
+[2026-01-09 19:09:00] Epoch: 35 | Iter: 000141 | Step Time: 3.521s | Local Loss: 0.5454 | Avg Loss: 0.5454 | LR: 0.000010
+[2026-01-09 19:09:03] Epoch: 35 | Iter: 000142 | Step Time: 3.597s | Local Loss: 0.1692 | Avg Loss: 0.1692 | LR: 0.000010
+[2026-01-09 19:09:07] Epoch: 35 | Iter: 000143 | Step Time: 3.567s | Local Loss: 0.2043 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-09 19:09:13] Epoch: 36 | Iter: 000144 | Step Time: 3.647s | Local Loss: 0.5424 | Avg Loss: 0.5424 | LR: 0.000010
+[2026-01-09 19:09:17] Epoch: 36 | Iter: 000145 | Step Time: 3.862s | Local Loss: 0.0784 | Avg Loss: 0.0784 | LR: 0.000010
+[2026-01-09 19:09:21] Epoch: 36 | Iter: 000146 | Step Time: 3.556s | Local Loss: 0.4047 | Avg Loss: 0.4047 | LR: 0.000010
+[2026-01-09 19:09:24] Epoch: 36 | Iter: 000147 | Step Time: 3.538s | Local Loss: 0.2214 | Avg Loss: 0.2214 | LR: 0.000010
+[2026-01-09 19:09:31] Epoch: 37 | Iter: 000148 | Step Time: 3.977s | Local Loss: 0.0693 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-09 19:09:35] Epoch: 37 | Iter: 000149 | Step Time: 3.568s | Local Loss: 0.1168 | Avg Loss: 0.1168 | LR: 0.000010
+[2026-01-09 19:10:38] Epoch: 37 | Iter: 000150 | Step Time: 3.632s | Local Loss: 0.1279 | Avg Loss: 0.1279 | LR: 0.000010
+[2026-01-09 19:10:42] Epoch: 37 | Iter: 000151 | Step Time: 3.852s | Local Loss: 0.2709 | Avg Loss: 0.2709 | LR: 0.000010
+[2026-01-09 19:10:48] Epoch: 38 | Iter: 000152 | Step Time: 3.761s | Local Loss: 0.1525 | Avg Loss: 0.1525 | LR: 0.000010
+[2026-01-09 19:10:52] Epoch: 38 | Iter: 000153 | Step Time: 3.547s | Local Loss: 0.2740 | Avg Loss: 0.2740 | LR: 0.000010
+[2026-01-09 19:10:55] Epoch: 38 | Iter: 000154 | Step Time: 3.513s | Local Loss: 0.2021 | Avg Loss: 0.2021 | LR: 0.000010
+[2026-01-09 19:10:59] Epoch: 38 | Iter: 000155 | Step Time: 3.543s | Local Loss: 0.0154 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-09 19:11:05] Epoch: 39 | Iter: 000156 | Step Time: 3.581s | Local Loss: 0.1369 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-09 19:11:08] Epoch: 39 | Iter: 000157 | Step Time: 3.505s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 19:11:12] Epoch: 39 | Iter: 000158 | Step Time: 3.537s | Local Loss: 0.1608 | Avg Loss: 0.1608 | LR: 0.000010
+[2026-01-09 19:11:16] Epoch: 39 | Iter: 000159 | Step Time: 3.505s | Local Loss: 0.3967 | Avg Loss: 0.3967 | LR: 0.000010
+[2026-01-09 19:11:22] Epoch: 40 | Iter: 000160 | Step Time: 3.713s | Local Loss: 0.1065 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-09 19:11:25] Epoch: 40 | Iter: 000161 | Step Time: 3.539s | Local Loss: 0.4061 | Avg Loss: 0.4061 | LR: 0.000010
+[2026-01-09 19:11:29] Epoch: 40 | Iter: 000162 | Step Time: 3.643s | Local Loss: 0.3322 | Avg Loss: 0.3322 | LR: 0.000010
+[2026-01-09 19:11:33] Epoch: 40 | Iter: 000163 | Step Time: 3.525s | Local Loss: 0.0608 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-09 19:11:39] Epoch: 41 | Iter: 000164 | Step Time: 3.564s | Local Loss: 0.4030 | Avg Loss: 0.4030 | LR: 0.000010
+[2026-01-09 19:11:42] Epoch: 41 | Iter: 000165 | Step Time: 3.579s | Local Loss: 0.0831 | Avg Loss: 0.0831 | LR: 0.000010
+[2026-01-09 19:11:46] Epoch: 41 | Iter: 000166 | Step Time: 3.547s | Local Loss: 0.5460 | Avg Loss: 0.5460 | LR: 0.000010
+[2026-01-09 19:11:50] Epoch: 41 | Iter: 000167 | Step Time: 3.586s | Local Loss: 0.0717 | Avg Loss: 0.0717 | LR: 0.000010
+[2026-01-09 19:11:56] Epoch: 42 | Iter: 000168 | Step Time: 3.780s | Local Loss: 0.3778 | Avg Loss: 0.3778 | LR: 0.000010
+[2026-01-09 19:11:59] Epoch: 42 | Iter: 000169 | Step Time: 3.540s | Local Loss: 0.0317 | Avg Loss: 0.0317 | LR: 0.000010
+[2026-01-09 19:12:03] Epoch: 42 | Iter: 000170 | Step Time: 3.546s | Local Loss: 0.0466 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-09 19:12:07] Epoch: 42 | Iter: 000171 | Step Time: 3.534s | Local Loss: 0.1790 | Avg Loss: 0.1790 | LR: 0.000010
+[2026-01-09 19:12:13] Epoch: 43 | Iter: 000172 | Step Time: 3.760s | Local Loss: 0.0232 | Avg Loss: 0.0232 | LR: 0.000010
+[2026-01-09 19:12:16] Epoch: 43 | Iter: 000173 | Step Time: 3.529s | Local Loss: 0.0443 | Avg Loss: 0.0443 | LR: 0.000010
+[2026-01-09 19:12:20] Epoch: 43 | Iter: 000174 | Step Time: 3.549s | Local Loss: 0.0624 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-09 19:12:24] Epoch: 43 | Iter: 000175 | Step Time: 3.568s | Local Loss: 0.2092 | Avg Loss: 0.2092 | LR: 0.000010
+[2026-01-09 19:12:30] Epoch: 44 | Iter: 000176 | Step Time: 3.884s | Local Loss: 0.2775 | Avg Loss: 0.2775 | LR: 0.000010
+[2026-01-09 19:12:34] Epoch: 44 | Iter: 000177 | Step Time: 3.557s | Local Loss: 0.1384 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-09 19:12:37] Epoch: 44 | Iter: 000178 | Step Time: 3.514s | Local Loss: 0.5993 | Avg Loss: 0.5993 | LR: 0.000010
+[2026-01-09 19:12:41] Epoch: 44 | Iter: 000179 | Step Time: 3.543s | Local Loss: 0.4816 | Avg Loss: 0.4816 | LR: 0.000010
+[2026-01-09 19:12:47] Epoch: 45 | Iter: 000180 | Step Time: 3.838s | Local Loss: 0.4490 | Avg Loss: 0.4490 | LR: 0.000010
+[2026-01-09 19:12:51] Epoch: 45 | Iter: 000181 | Step Time: 3.522s | Local Loss: 0.0949 | Avg Loss: 0.0949 | LR: 0.000010
+[2026-01-09 19:12:54] Epoch: 45 | Iter: 000182 | Step Time: 3.523s | Local Loss: 0.4416 | Avg Loss: 0.4416 | LR: 0.000010
+[2026-01-09 19:12:58] Epoch: 45 | Iter: 000183 | Step Time: 3.534s | Local Loss: 0.0678 | Avg Loss: 0.0678 | LR: 0.000010
+[2026-01-09 19:13:04] Epoch: 46 | Iter: 000184 | Step Time: 3.874s | Local Loss: 0.2211 | Avg Loss: 0.2211 | LR: 0.000010
+[2026-01-09 19:13:08] Epoch: 46 | Iter: 000185 | Step Time: 3.543s | Local Loss: 0.5114 | Avg Loss: 0.5114 | LR: 0.000010
+[2026-01-09 19:13:12] Epoch: 46 | Iter: 000186 | Step Time: 3.529s | Local Loss: 0.1003 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-09 19:13:15] Epoch: 46 | Iter: 000187 | Step Time: 3.529s | Local Loss: 0.1232 | Avg Loss: 0.1232 | LR: 0.000010
+[2026-01-09 19:13:22] Epoch: 47 | Iter: 000188 | Step Time: 3.858s | Local Loss: 0.0867 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-09 19:13:25] Epoch: 47 | Iter: 000189 | Step Time: 3.690s | Local Loss: 0.1701 | Avg Loss: 0.1701 | LR: 0.000010
+[2026-01-09 19:13:29] Epoch: 47 | Iter: 000190 | Step Time: 3.528s | Local Loss: 0.1347 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-09 19:13:32] Epoch: 47 | Iter: 000191 | Step Time: 3.540s | Local Loss: 0.0331 | Avg Loss: 0.0331 | LR: 0.000010
+[2026-01-09 19:13:38] Epoch: 48 | Iter: 000192 | Step Time: 3.744s | Local Loss: 0.4788 | Avg Loss: 0.4788 | LR: 0.000010
+[2026-01-09 19:13:42] Epoch: 48 | Iter: 000193 | Step Time: 3.514s | Local Loss: 0.1327 | Avg Loss: 0.1327 | LR: 0.000010
+[2026-01-09 19:13:46] Epoch: 48 | Iter: 000194 | Step Time: 3.524s | Local Loss: 0.2715 | Avg Loss: 0.2715 | LR: 0.000010
+[2026-01-09 19:13:49] Epoch: 48 | Iter: 000195 | Step Time: 3.543s | Local Loss: 0.3549 | Avg Loss: 0.3549 | LR: 0.000010
+[2026-01-09 19:13:56] Epoch: 49 | Iter: 000196 | Step Time: 3.595s | Local Loss: 0.1022 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-09 19:13:59] Epoch: 49 | Iter: 000197 | Step Time: 3.520s | Local Loss: 0.0533 | Avg Loss: 0.0533 | LR: 0.000010
+[2026-01-09 19:14:03] Epoch: 49 | Iter: 000198 | Step Time: 3.530s | Local Loss: 0.4634 | Avg Loss: 0.4634 | LR: 0.000010
+[2026-01-09 19:14:06] Epoch: 49 | Iter: 000199 | Step Time: 3.509s | Local Loss: 0.1989 | Avg Loss: 0.1989 | LR: 0.000010
+[2026-01-09 19:14:13] Epoch: 50 | Iter: 000200 | Step Time: 3.583s | Local Loss: 0.0095 | Avg Loss: 0.0095 | LR: 0.000010
+[2026-01-09 19:14:16] Epoch: 50 | Iter: 000201 | Step Time: 3.542s | Local Loss: 0.0976 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-09 19:14:20] Epoch: 50 | Iter: 000202 | Step Time: 3.514s | Local Loss: 0.4393 | Avg Loss: 0.4393 | LR: 0.000010
+[2026-01-09 19:14:23] Epoch: 50 | Iter: 000203 | Step Time: 3.543s | Local Loss: 0.2669 | Avg Loss: 0.2669 | LR: 0.000010
+[2026-01-09 19:14:29] Epoch: 51 | Iter: 000204 | Step Time: 3.579s | Local Loss: 0.3935 | Avg Loss: 0.3935 | LR: 0.000010
+[2026-01-09 19:14:33] Epoch: 51 | Iter: 000205 | Step Time: 3.508s | Local Loss: 0.1476 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-09 19:14:37] Epoch: 51 | Iter: 000206 | Step Time: 3.798s | Local Loss: 0.1080 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-09 19:14:40] Epoch: 51 | Iter: 000207 | Step Time: 3.561s | Local Loss: 0.0981 | Avg Loss: 0.0981 | LR: 0.000010
+[2026-01-09 19:14:47] Epoch: 52 | Iter: 000208 | Step Time: 3.716s | Local Loss: 0.2921 | Avg Loss: 0.2921 | LR: 0.000010
+[2026-01-09 19:14:50] Epoch: 52 | Iter: 000209 | Step Time: 3.527s | Local Loss: 0.0591 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-09 19:14:54] Epoch: 52 | Iter: 000210 | Step Time: 3.539s | Local Loss: 0.0725 | Avg Loss: 0.0725 | LR: 0.000010
+[2026-01-09 19:14:57] Epoch: 52 | Iter: 000211 | Step Time: 3.508s | Local Loss: 0.0220 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-09 19:15:03] Epoch: 53 | Iter: 000212 | Step Time: 3.576s | Local Loss: 0.1450 | Avg Loss: 0.1450 | LR: 0.000010
+[2026-01-09 19:15:07] Epoch: 53 | Iter: 000213 | Step Time: 3.541s | Local Loss: 0.0408 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-09 19:15:10] Epoch: 53 | Iter: 000214 | Step Time: 3.540s | Local Loss: 0.0441 | Avg Loss: 0.0441 | LR: 0.000010
+[2026-01-09 19:15:14] Epoch: 53 | Iter: 000215 | Step Time: 3.535s | Local Loss: 0.1042 | Avg Loss: 0.1042 | LR: 0.000010
+[2026-01-09 19:15:20] Epoch: 54 | Iter: 000216 | Step Time: 3.745s | Local Loss: 0.0327 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-09 19:15:24] Epoch: 54 | Iter: 000217 | Step Time: 3.677s | Local Loss: 0.0328 | Avg Loss: 0.0328 | LR: 0.000010
+[2026-01-09 19:15:27] Epoch: 54 | Iter: 000218 | Step Time: 3.520s | Local Loss: 0.3543 | Avg Loss: 0.3543 | LR: 0.000010
+[2026-01-09 19:15:32] Epoch: 54 | Iter: 000219 | Step Time: 4.047s | Local Loss: 0.1079 | Avg Loss: 0.1079 | LR: 0.000010
+[2026-01-09 19:15:38] Epoch: 55 | Iter: 000220 | Step Time: 3.661s | Local Loss: 0.0211 | Avg Loss: 0.0211 | LR: 0.000010
+[2026-01-09 19:15:42] Epoch: 55 | Iter: 000221 | Step Time: 3.596s | Local Loss: 0.4173 | Avg Loss: 0.4173 | LR: 0.000010
+[2026-01-09 19:15:45] Epoch: 55 | Iter: 000222 | Step Time: 3.537s | Local Loss: 0.1259 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-09 19:15:49] Epoch: 55 | Iter: 000223 | Step Time: 3.660s | Local Loss: 0.4323 | Avg Loss: 0.4323 | LR: 0.000010
+[2026-01-09 19:15:55] Epoch: 56 | Iter: 000224 | Step Time: 3.578s | Local Loss: 0.1330 | Avg Loss: 0.1330 | LR: 0.000010
+[2026-01-09 19:15:59] Epoch: 56 | Iter: 000225 | Step Time: 3.569s | Local Loss: 0.4199 | Avg Loss: 0.4199 | LR: 0.000010
+[2026-01-09 19:16:02] Epoch: 56 | Iter: 000226 | Step Time: 3.538s | Local Loss: 0.4709 | Avg Loss: 0.4709 | LR: 0.000010
+[2026-01-09 19:16:06] Epoch: 56 | Iter: 000227 | Step Time: 3.564s | Local Loss: 0.2263 | Avg Loss: 0.2263 | LR: 0.000010
+[2026-01-09 19:16:12] Epoch: 57 | Iter: 000228 | Step Time: 3.778s | Local Loss: 0.1527 | Avg Loss: 0.1527 | LR: 0.000010
+[2026-01-09 19:16:16] Epoch: 57 | Iter: 000229 | Step Time: 3.525s | Local Loss: 0.1307 | Avg Loss: 0.1307 | LR: 0.000010
+[2026-01-09 19:16:20] Epoch: 57 | Iter: 000230 | Step Time: 3.655s | Local Loss: 0.4814 | Avg Loss: 0.4814 | LR: 0.000010
+[2026-01-09 19:16:23] Epoch: 57 | Iter: 000231 | Step Time: 3.525s | Local Loss: 0.4747 | Avg Loss: 0.4747 | LR: 0.000010
+[2026-01-09 19:16:29] Epoch: 58 | Iter: 000232 | Step Time: 3.561s | Local Loss: 0.0666 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-09 19:16:34] Epoch: 58 | Iter: 000233 | Step Time: 4.318s | Local Loss: 0.0978 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-09 19:16:37] Epoch: 58 | Iter: 000234 | Step Time: 3.546s | Local Loss: 0.0636 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-09 19:16:41] Epoch: 58 | Iter: 000235 | Step Time: 3.572s | Local Loss: 0.0683 | Avg Loss: 0.0683 | LR: 0.000010
+[2026-01-09 19:16:47] Epoch: 59 | Iter: 000236 | Step Time: 3.708s | Local Loss: 0.1153 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-09 19:16:50] Epoch: 59 | Iter: 000237 | Step Time: 3.563s | Local Loss: 0.3966 | Avg Loss: 0.3966 | LR: 0.000010
+[2026-01-09 19:16:54] Epoch: 59 | Iter: 000238 | Step Time: 3.519s | Local Loss: 0.2212 | Avg Loss: 0.2212 | LR: 0.000010
+[2026-01-09 19:16:58] Epoch: 59 | Iter: 000239 | Step Time: 3.529s | Local Loss: 0.4114 | Avg Loss: 0.4114 | LR: 0.000010
+[2026-01-09 19:17:04] Epoch: 60 | Iter: 000240 | Step Time: 3.864s | Local Loss: 0.2352 | Avg Loss: 0.2352 | LR: 0.000010
+[2026-01-09 19:17:08] Epoch: 60 | Iter: 000241 | Step Time: 3.516s | Local Loss: 0.5112 | Avg Loss: 0.5112 | LR: 0.000010
+[2026-01-09 19:17:11] Epoch: 60 | Iter: 000242 | Step Time: 3.534s | Local Loss: 0.1297 | Avg Loss: 0.1297 | LR: 0.000010
+[2026-01-09 19:17:15] Epoch: 60 | Iter: 000243 | Step Time: 3.548s | Local Loss: 0.2942 | Avg Loss: 0.2942 | LR: 0.000010
+[2026-01-09 19:17:21] Epoch: 61 | Iter: 000244 | Step Time: 3.525s | Local Loss: 0.5072 | Avg Loss: 0.5072 | LR: 0.000010
+[2026-01-09 19:17:25] Epoch: 61 | Iter: 000245 | Step Time: 3.631s | Local Loss: 0.1135 | Avg Loss: 0.1135 | LR: 0.000010
+[2026-01-09 19:17:28] Epoch: 61 | Iter: 000246 | Step Time: 3.500s | Local Loss: 0.4684 | Avg Loss: 0.4684 | LR: 0.000010
+[2026-01-09 19:17:32] Epoch: 61 | Iter: 000247 | Step Time: 3.576s | Local Loss: 0.1355 | Avg Loss: 0.1355 | LR: 0.000010
+[2026-01-09 19:17:38] Epoch: 62 | Iter: 000248 | Step Time: 3.865s | Local Loss: 0.0974 | Avg Loss: 0.0974 | LR: 0.000010
+[2026-01-09 19:17:42] Epoch: 62 | Iter: 000249 | Step Time: 3.529s | Local Loss: 0.3068 | Avg Loss: 0.3068 | LR: 0.000010
+[2026-01-09 19:17:46] Epoch: 62 | Iter: 000250 | Step Time: 3.535s | Local Loss: 0.0433 | Avg Loss: 0.0433 | LR: 0.000010
+[2026-01-09 19:17:49] Epoch: 62 | Iter: 000251 | Step Time: 3.536s | Local Loss: 0.2695 | Avg Loss: 0.2695 | LR: 0.000010
+[2026-01-09 19:17:56] Epoch: 63 | Iter: 000252 | Step Time: 3.690s | Local Loss: 0.4260 | Avg Loss: 0.4260 | LR: 0.000010
+[2026-01-09 19:17:59] Epoch: 63 | Iter: 000253 | Step Time: 3.539s | Local Loss: 0.4141 | Avg Loss: 0.4141 | LR: 0.000010
+[2026-01-09 19:18:03] Epoch: 63 | Iter: 000254 | Step Time: 3.506s | Local Loss: 0.1039 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-09 19:18:06] Epoch: 63 | Iter: 000255 | Step Time: 3.538s | Local Loss: 0.5808 | Avg Loss: 0.5808 | LR: 0.000010
+[2026-01-09 19:18:12] Epoch: 64 | Iter: 000256 | Step Time: 3.568s | Local Loss: 0.0521 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-09 19:18:16] Epoch: 64 | Iter: 000257 | Step Time: 3.538s | Local Loss: 0.2376 | Avg Loss: 0.2376 | LR: 0.000010
+[2026-01-09 19:18:19] Epoch: 64 | Iter: 000258 | Step Time: 3.525s | Local Loss: 0.4628 | Avg Loss: 0.4628 | LR: 0.000010
+[2026-01-09 19:18:23] Epoch: 64 | Iter: 000259 | Step Time: 3.528s | Local Loss: 0.0210 | Avg Loss: 0.0210 | LR: 0.000010
+[2026-01-09 19:18:29] Epoch: 65 | Iter: 000260 | Step Time: 3.826s | Local Loss: 0.1096 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-09 19:18:33] Epoch: 65 | Iter: 000261 | Step Time: 3.556s | Local Loss: 0.1663 | Avg Loss: 0.1663 | LR: 0.000010
+[2026-01-09 19:18:37] Epoch: 65 | Iter: 000262 | Step Time: 3.565s | Local Loss: 0.5115 | Avg Loss: 0.5115 | LR: 0.000010
+[2026-01-09 19:18:40] Epoch: 65 | Iter: 000263 | Step Time: 3.553s | Local Loss: 0.1186 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-09 19:18:46] Epoch: 66 | Iter: 000264 | Step Time: 3.543s | Local Loss: 0.4460 | Avg Loss: 0.4460 | LR: 0.000010
+[2026-01-09 19:18:50] Epoch: 66 | Iter: 000265 | Step Time: 3.526s | Local Loss: 0.0853 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-09 19:18:53] Epoch: 66 | Iter: 000266 | Step Time: 3.530s | Local Loss: 0.0530 | Avg Loss: 0.0530 | LR: 0.000010
+[2026-01-09 19:18:57] Epoch: 66 | Iter: 000267 | Step Time: 3.569s | Local Loss: 0.1805 | Avg Loss: 0.1805 | LR: 0.000010
+[2026-01-09 19:19:03] Epoch: 67 | Iter: 000268 | Step Time: 3.850s | Local Loss: 0.2927 | Avg Loss: 0.2927 | LR: 0.000010
+[2026-01-09 19:19:07] Epoch: 67 | Iter: 000269 | Step Time: 3.510s | Local Loss: 0.0202 | Avg Loss: 0.0202 | LR: 0.000010
+[2026-01-09 19:19:10] Epoch: 67 | Iter: 000270 | Step Time: 3.523s | Local Loss: 0.1958 | Avg Loss: 0.1958 | LR: 0.000010
+[2026-01-09 19:19:14] Epoch: 67 | Iter: 000271 | Step Time: 3.546s | Local Loss: 0.1331 | Avg Loss: 0.1331 | LR: 0.000010
+[2026-01-09 19:19:20] Epoch: 68 | Iter: 000272 | Step Time: 3.604s | Local Loss: 0.4962 | Avg Loss: 0.4962 | LR: 0.000010
+[2026-01-09 19:19:24] Epoch: 68 | Iter: 000273 | Step Time: 3.723s | Local Loss: 0.4019 | Avg Loss: 0.4019 | LR: 0.000010
+[2026-01-09 19:19:27] Epoch: 68 | Iter: 000274 | Step Time: 3.550s | Local Loss: 0.0388 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-09 19:19:31] Epoch: 68 | Iter: 000275 | Step Time: 3.583s | Local Loss: 0.5497 | Avg Loss: 0.5497 | LR: 0.000010
+[2026-01-09 19:19:37] Epoch: 69 | Iter: 000276 | Step Time: 3.767s | Local Loss: 0.3970 | Avg Loss: 0.3970 | LR: 0.000010
+[2026-01-09 19:19:41] Epoch: 69 | Iter: 000277 | Step Time: 3.536s | Local Loss: 0.1867 | Avg Loss: 0.1867 | LR: 0.000010
+[2026-01-09 19:19:45] Epoch: 69 | Iter: 000278 | Step Time: 3.519s | Local Loss: 0.1901 | Avg Loss: 0.1901 | LR: 0.000010
+[2026-01-09 19:19:48] Epoch: 69 | Iter: 000279 | Step Time: 3.555s | Local Loss: 0.0574 | Avg Loss: 0.0574 | LR: 0.000010
+[2026-01-09 19:19:54] Epoch: 70 | Iter: 000280 | Step Time: 3.549s | Local Loss: 0.1961 | Avg Loss: 0.1961 | LR: 0.000010
+[2026-01-09 19:19:58] Epoch: 70 | Iter: 000281 | Step Time: 3.547s | Local Loss: 0.3218 | Avg Loss: 0.3218 | LR: 0.000010
+[2026-01-09 19:20:01] Epoch: 70 | Iter: 000282 | Step Time: 3.568s | Local Loss: 0.1079 | Avg Loss: 0.1079 | LR: 0.000010
+[2026-01-09 19:20:05] Epoch: 70 | Iter: 000283 | Step Time: 3.553s | Local Loss: 0.0143 | Avg Loss: 0.0143 | LR: 0.000010
+[2026-01-09 19:20:11] Epoch: 71 | Iter: 000284 | Step Time: 3.708s | Local Loss: 0.0785 | Avg Loss: 0.0785 | LR: 0.000010
+[2026-01-09 19:20:14] Epoch: 71 | Iter: 000285 | Step Time: 3.517s | Local Loss: 0.2889 | Avg Loss: 0.2889 | LR: 0.000010
+[2026-01-09 19:20:18] Epoch: 71 | Iter: 000286 | Step Time: 3.583s | Local Loss: 0.4683 | Avg Loss: 0.4683 | LR: 0.000010
+[2026-01-09 19:20:22] Epoch: 71 | Iter: 000287 | Step Time: 3.549s | Local Loss: 0.1419 | Avg Loss: 0.1419 | LR: 0.000010
+[2026-01-09 19:20:28] Epoch: 72 | Iter: 000288 | Step Time: 3.738s | Local Loss: 0.2338 | Avg Loss: 0.2338 | LR: 0.000010
+[2026-01-09 19:20:32] Epoch: 72 | Iter: 000289 | Step Time: 3.542s | Local Loss: 0.1044 | Avg Loss: 0.1044 | LR: 0.000010
+[2026-01-09 19:20:36] Epoch: 72 | Iter: 000290 | Step Time: 3.552s | Local Loss: 0.0863 | Avg Loss: 0.0863 | LR: 0.000010
+[2026-01-09 19:20:39] Epoch: 72 | Iter: 000291 | Step Time: 3.537s | Local Loss: 0.5619 | Avg Loss: 0.5619 | LR: 0.000010
+[2026-01-09 19:20:46] Epoch: 73 | Iter: 000292 | Step Time: 3.904s | Local Loss: 0.3090 | Avg Loss: 0.3090 | LR: 0.000010
+[2026-01-09 19:20:49] Epoch: 73 | Iter: 000293 | Step Time: 3.547s | Local Loss: 0.1512 | Avg Loss: 0.1512 | LR: 0.000010
+[2026-01-09 19:20:53] Epoch: 73 | Iter: 000294 | Step Time: 3.544s | Local Loss: 0.2737 | Avg Loss: 0.2737 | LR: 0.000010
+[2026-01-09 19:20:56] Epoch: 73 | Iter: 000295 | Step Time: 3.513s | Local Loss: 0.0803 | Avg Loss: 0.0803 | LR: 0.000010
+[2026-01-09 19:21:02] Epoch: 74 | Iter: 000296 | Step Time: 3.855s | Local Loss: 0.0580 | Avg Loss: 0.0580 | LR: 0.000010
+[2026-01-09 19:21:06] Epoch: 74 | Iter: 000297 | Step Time: 3.571s | Local Loss: 0.5794 | Avg Loss: 0.5794 | LR: 0.000010
+[2026-01-09 19:21:10] Epoch: 74 | Iter: 000298 | Step Time: 3.520s | Local Loss: 0.2120 | Avg Loss: 0.2120 | LR: 0.000010
+[2026-01-09 19:21:13] Epoch: 74 | Iter: 000299 | Step Time: 3.542s | Local Loss: 0.1641 | Avg Loss: 0.1641 | LR: 0.000010
+[2026-01-09 19:22:17] Epoch: 75 | Iter: 000300 | Step Time: 3.778s | Local Loss: 0.0574 | Avg Loss: 0.0574 | LR: 0.000010
+[2026-01-09 19:22:21] Epoch: 75 | Iter: 000301 | Step Time: 3.512s | Local Loss: 0.0193 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-09 19:22:25] Epoch: 75 | Iter: 000302 | Step Time: 3.533s | Local Loss: 0.2144 | Avg Loss: 0.2144 | LR: 0.000010
+[2026-01-09 19:22:28] Epoch: 75 | Iter: 000303 | Step Time: 3.508s | Local Loss: 0.2333 | Avg Loss: 0.2333 | LR: 0.000010
+[2026-01-09 19:22:34] Epoch: 76 | Iter: 000304 | Step Time: 3.583s | Local Loss: 0.0373 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-09 19:22:38] Epoch: 76 | Iter: 000305 | Step Time: 3.562s | Local Loss: 0.1806 | Avg Loss: 0.1806 | LR: 0.000010
+[2026-01-09 19:22:41] Epoch: 76 | Iter: 000306 | Step Time: 3.530s | Local Loss: 0.1659 | Avg Loss: 0.1659 | LR: 0.000010
+[2026-01-09 19:22:45] Epoch: 76 | Iter: 000307 | Step Time: 3.520s | Local Loss: 0.2458 | Avg Loss: 0.2458 | LR: 0.000010
+[2026-01-09 19:22:51] Epoch: 77 | Iter: 000308 | Step Time: 3.529s | Local Loss: 0.4983 | Avg Loss: 0.4983 | LR: 0.000010
+[2026-01-09 19:22:54] Epoch: 77 | Iter: 000309 | Step Time: 3.537s | Local Loss: 0.4451 | Avg Loss: 0.4451 | LR: 0.000010
+[2026-01-09 19:22:58] Epoch: 77 | Iter: 000310 | Step Time: 3.523s | Local Loss: 0.1772 | Avg Loss: 0.1772 | LR: 0.000010
+[2026-01-09 19:23:02] Epoch: 77 | Iter: 000311 | Step Time: 3.503s | Local Loss: 0.3886 | Avg Loss: 0.3886 | LR: 0.000010
+[2026-01-09 19:23:08] Epoch: 78 | Iter: 000312 | Step Time: 3.663s | Local Loss: 0.1185 | Avg Loss: 0.1185 | LR: 0.000010
+[2026-01-09 19:23:11] Epoch: 78 | Iter: 000313 | Step Time: 3.659s | Local Loss: 0.2532 | Avg Loss: 0.2532 | LR: 0.000010
+[2026-01-09 19:23:15] Epoch: 78 | Iter: 000314 | Step Time: 3.656s | Local Loss: 0.1249 | Avg Loss: 0.1249 | LR: 0.000010
+[2026-01-09 19:23:19] Epoch: 78 | Iter: 000315 | Step Time: 3.539s | Local Loss: 0.1553 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-09 19:23:25] Epoch: 79 | Iter: 000316 | Step Time: 3.569s | Local Loss: 0.3604 | Avg Loss: 0.3604 | LR: 0.000010
+[2026-01-09 19:23:28] Epoch: 79 | Iter: 000317 | Step Time: 3.550s | Local Loss: 0.1045 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-09 19:23:32] Epoch: 79 | Iter: 000318 | Step Time: 3.509s | Local Loss: 0.0209 | Avg Loss: 0.0209 | LR: 0.000010
+[2026-01-09 19:23:35] Epoch: 79 | Iter: 000319 | Step Time: 3.519s | Local Loss: 0.4794 | Avg Loss: 0.4794 | LR: 0.000010
+[2026-01-09 19:23:42] Epoch: 80 | Iter: 000320 | Step Time: 3.577s | Local Loss: 0.0498 | Avg Loss: 0.0498 | LR: 0.000010
+[2026-01-09 19:23:45] Epoch: 80 | Iter: 000321 | Step Time: 3.526s | Local Loss: 0.1523 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-09 19:23:49] Epoch: 80 | Iter: 000322 | Step Time: 3.525s | Local Loss: 0.5997 | Avg Loss: 0.5997 | LR: 0.000010
+[2026-01-09 19:23:52] Epoch: 80 | Iter: 000323 | Step Time: 3.537s | Local Loss: 0.1185 | Avg Loss: 0.1185 | LR: 0.000010
+[2026-01-09 19:23:58] Epoch: 81 | Iter: 000324 | Step Time: 3.515s | Local Loss: 0.1703 | Avg Loss: 0.1703 | LR: 0.000010
+[2026-01-09 19:24:02] Epoch: 81 | Iter: 000325 | Step Time: 3.600s | Local Loss: 0.4528 | Avg Loss: 0.4528 | LR: 0.000010
+[2026-01-09 19:24:06] Epoch: 81 | Iter: 000326 | Step Time: 3.536s | Local Loss: 0.1704 | Avg Loss: 0.1704 | LR: 0.000010
+[2026-01-09 19:24:09] Epoch: 81 | Iter: 000327 | Step Time: 3.561s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 19:24:15] Epoch: 82 | Iter: 000328 | Step Time: 3.556s | Local Loss: 0.0081 | Avg Loss: 0.0081 | LR: 0.000010
+[2026-01-09 19:24:19] Epoch: 82 | Iter: 000329 | Step Time: 3.499s | Local Loss: 0.0865 | Avg Loss: 0.0865 | LR: 0.000010
+[2026-01-09 19:24:22] Epoch: 82 | Iter: 000330 | Step Time: 3.525s | Local Loss: 0.0686 | Avg Loss: 0.0686 | LR: 0.000010
+[2026-01-09 19:24:27] Epoch: 82 | Iter: 000331 | Step Time: 4.191s | Local Loss: 0.1089 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-09 19:24:33] Epoch: 83 | Iter: 000332 | Step Time: 3.849s | Local Loss: 0.4419 | Avg Loss: 0.4419 | LR: 0.000010
+[2026-01-09 19:24:36] Epoch: 83 | Iter: 000333 | Step Time: 3.523s | Local Loss: 0.0580 | Avg Loss: 0.0580 | LR: 0.000010
+[2026-01-09 19:24:40] Epoch: 83 | Iter: 000334 | Step Time: 3.501s | Local Loss: 0.5612 | Avg Loss: 0.5612 | LR: 0.000010
+[2026-01-09 19:24:44] Epoch: 83 | Iter: 000335 | Step Time: 3.546s | Local Loss: 0.3084 | Avg Loss: 0.3084 | LR: 0.000010
+[2026-01-09 19:24:50] Epoch: 84 | Iter: 000336 | Step Time: 3.871s | Local Loss: 0.0308 | Avg Loss: 0.0308 | LR: 0.000010
+[2026-01-09 19:24:54] Epoch: 84 | Iter: 000337 | Step Time: 3.551s | Local Loss: 0.1419 | Avg Loss: 0.1419 | LR: 0.000010
+[2026-01-09 19:24:57] Epoch: 84 | Iter: 000338 | Step Time: 3.516s | Local Loss: 0.1624 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-09 19:25:01] Epoch: 84 | Iter: 000339 | Step Time: 3.552s | Local Loss: 0.0718 | Avg Loss: 0.0718 | LR: 0.000010
+[2026-01-09 19:25:07] Epoch: 85 | Iter: 000340 | Step Time: 3.827s | Local Loss: 0.1302 | Avg Loss: 0.1302 | LR: 0.000010
+[2026-01-09 19:25:11] Epoch: 85 | Iter: 000341 | Step Time: 3.533s | Local Loss: 0.3650 | Avg Loss: 0.3650 | LR: 0.000010
+[2026-01-09 19:25:14] Epoch: 85 | Iter: 000342 | Step Time: 3.570s | Local Loss: 0.2447 | Avg Loss: 0.2447 | LR: 0.000010
+[2026-01-09 19:25:18] Epoch: 85 | Iter: 000343 | Step Time: 3.529s | Local Loss: 0.3120 | Avg Loss: 0.3120 | LR: 0.000010
+[2026-01-09 19:25:23] Epoch: 86 | Iter: 000344 | Step Time: 3.551s | Local Loss: 0.1421 | Avg Loss: 0.1421 | LR: 0.000010
+[2026-01-09 19:25:27] Epoch: 86 | Iter: 000345 | Step Time: 3.518s | Local Loss: 0.0816 | Avg Loss: 0.0816 | LR: 0.000010
+[2026-01-09 19:25:31] Epoch: 86 | Iter: 000346 | Step Time: 3.517s | Local Loss: 0.0989 | Avg Loss: 0.0989 | LR: 0.000010
+[2026-01-09 19:25:35] Epoch: 86 | Iter: 000347 | Step Time: 3.538s | Local Loss: 0.1673 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-09 19:25:41] Epoch: 87 | Iter: 000348 | Step Time: 3.846s | Local Loss: 0.3751 | Avg Loss: 0.3751 | LR: 0.000010
+[2026-01-09 19:25:44] Epoch: 87 | Iter: 000349 | Step Time: 3.533s | Local Loss: 0.4151 | Avg Loss: 0.4151 | LR: 0.000010
+[2026-01-09 19:25:48] Epoch: 87 | Iter: 000350 | Step Time: 3.522s | Local Loss: 0.4982 | Avg Loss: 0.4982 | LR: 0.000010
+[2026-01-09 19:25:52] Epoch: 87 | Iter: 000351 | Step Time: 3.572s | Local Loss: 0.1510 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-09 19:25:58] Epoch: 88 | Iter: 000352 | Step Time: 3.705s | Local Loss: 0.1153 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-09 19:26:01] Epoch: 88 | Iter: 000353 | Step Time: 3.509s | Local Loss: 0.1853 | Avg Loss: 0.1853 | LR: 0.000010
+[2026-01-09 19:26:05] Epoch: 88 | Iter: 000354 | Step Time: 3.539s | Local Loss: 0.3678 | Avg Loss: 0.3678 | LR: 0.000010
+[2026-01-09 19:26:09] Epoch: 88 | Iter: 000355 | Step Time: 3.520s | Local Loss: 0.3098 | Avg Loss: 0.3098 | LR: 0.000010
+[2026-01-09 19:26:14] Epoch: 89 | Iter: 000356 | Step Time: 3.584s | Local Loss: 0.0743 | Avg Loss: 0.0743 | LR: 0.000010
+[2026-01-09 19:26:18] Epoch: 89 | Iter: 000357 | Step Time: 3.504s | Local Loss: 0.0192 | Avg Loss: 0.0192 | LR: 0.000010
+[2026-01-09 19:26:22] Epoch: 89 | Iter: 000358 | Step Time: 3.529s | Local Loss: 0.3737 | Avg Loss: 0.3737 | LR: 0.000010
+[2026-01-09 19:26:25] Epoch: 89 | Iter: 000359 | Step Time: 3.553s | Local Loss: 0.4765 | Avg Loss: 0.4765 | LR: 0.000010
+[2026-01-09 19:26:32] Epoch: 90 | Iter: 000360 | Step Time: 3.754s | Local Loss: 0.0536 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-09 19:26:35] Epoch: 90 | Iter: 000361 | Step Time: 3.573s | Local Loss: 0.1252 | Avg Loss: 0.1252 | LR: 0.000010
+[2026-01-09 19:26:39] Epoch: 90 | Iter: 000362 | Step Time: 3.509s | Local Loss: 0.1106 | Avg Loss: 0.1106 | LR: 0.000010
+[2026-01-09 19:26:42] Epoch: 90 | Iter: 000363 | Step Time: 3.549s | Local Loss: 0.2768 | Avg Loss: 0.2768 | LR: 0.000010
+[2026-01-09 19:26:49] Epoch: 91 | Iter: 000364 | Step Time: 3.864s | Local Loss: 0.0078 | Avg Loss: 0.0078 | LR: 0.000010
+[2026-01-09 19:26:52] Epoch: 91 | Iter: 000365 | Step Time: 3.546s | Local Loss: 0.2920 | Avg Loss: 0.2920 | LR: 0.000010
+[2026-01-09 19:26:56] Epoch: 91 | Iter: 000366 | Step Time: 3.662s | Local Loss: 0.1517 | Avg Loss: 0.1517 | LR: 0.000010
+[2026-01-09 19:26:59] Epoch: 91 | Iter: 000367 | Step Time: 3.500s | Local Loss: 0.1836 | Avg Loss: 0.1836 | LR: 0.000010
+[2026-01-09 19:27:05] Epoch: 92 | Iter: 000368 | Step Time: 3.531s | Local Loss: 0.1025 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-09 19:27:09] Epoch: 92 | Iter: 000369 | Step Time: 3.541s | Local Loss: 0.1255 | Avg Loss: 0.1255 | LR: 0.000010
+[2026-01-09 19:27:12] Epoch: 92 | Iter: 000370 | Step Time: 3.532s | Local Loss: 0.0460 | Avg Loss: 0.0460 | LR: 0.000010
+[2026-01-09 19:27:16] Epoch: 92 | Iter: 000371 | Step Time: 3.568s | Local Loss: 0.0995 | Avg Loss: 0.0995 | LR: 0.000010
+[2026-01-09 19:27:22] Epoch: 93 | Iter: 000372 | Step Time: 3.534s | Local Loss: 0.5733 | Avg Loss: 0.5733 | LR: 0.000010
+[2026-01-09 19:27:26] Epoch: 93 | Iter: 000373 | Step Time: 3.525s | Local Loss: 0.0718 | Avg Loss: 0.0718 | LR: 0.000010
+[2026-01-09 19:27:29] Epoch: 93 | Iter: 000374 | Step Time: 3.515s | Local Loss: 0.4726 | Avg Loss: 0.4726 | LR: 0.000010
+[2026-01-09 19:27:33] Epoch: 93 | Iter: 000375 | Step Time: 3.561s | Local Loss: 0.4855 | Avg Loss: 0.4855 | LR: 0.000010
+[2026-01-09 19:27:39] Epoch: 94 | Iter: 000376 | Step Time: 3.825s | Local Loss: 0.1527 | Avg Loss: 0.1527 | LR: 0.000010
+[2026-01-09 19:27:43] Epoch: 94 | Iter: 000377 | Step Time: 3.541s | Local Loss: 0.1247 | Avg Loss: 0.1247 | LR: 0.000010
+[2026-01-09 19:27:47] Epoch: 94 | Iter: 000378 | Step Time: 3.695s | Local Loss: 0.0313 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-09 19:27:50] Epoch: 94 | Iter: 000379 | Step Time: 3.558s | Local Loss: 0.2444 | Avg Loss: 0.2444 | LR: 0.000010
+[2026-01-09 19:27:56] Epoch: 95 | Iter: 000380 | Step Time: 3.561s | Local Loss: 0.4015 | Avg Loss: 0.4015 | LR: 0.000010
+[2026-01-09 19:28:00] Epoch: 95 | Iter: 000381 | Step Time: 3.508s | Local Loss: 0.0481 | Avg Loss: 0.0481 | LR: 0.000010
+[2026-01-09 19:28:03] Epoch: 95 | Iter: 000382 | Step Time: 3.562s | Local Loss: 0.2321 | Avg Loss: 0.2321 | LR: 0.000010
+[2026-01-09 19:28:07] Epoch: 95 | Iter: 000383 | Step Time: 3.631s | Local Loss: 0.2354 | Avg Loss: 0.2354 | LR: 0.000010
+[2026-01-09 19:28:13] Epoch: 96 | Iter: 000384 | Step Time: 3.567s | Local Loss: 0.0739 | Avg Loss: 0.0739 | LR: 0.000010
+[2026-01-09 19:28:16] Epoch: 96 | Iter: 000385 | Step Time: 3.505s | Local Loss: 0.0753 | Avg Loss: 0.0753 | LR: 0.000010
+[2026-01-09 19:28:20] Epoch: 96 | Iter: 000386 | Step Time: 3.485s | Local Loss: 0.0319 | Avg Loss: 0.0319 | LR: 0.000010
+[2026-01-09 19:28:24] Epoch: 96 | Iter: 000387 | Step Time: 3.529s | Local Loss: 0.4067 | Avg Loss: 0.4067 | LR: 0.000010
+[2026-01-09 19:28:29] Epoch: 97 | Iter: 000388 | Step Time: 3.727s | Local Loss: 0.0564 | Avg Loss: 0.0564 | LR: 0.000010
+[2026-01-09 19:28:33] Epoch: 97 | Iter: 000389 | Step Time: 3.539s | Local Loss: 0.0458 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-09 19:28:37] Epoch: 97 | Iter: 000390 | Step Time: 3.520s | Local Loss: 0.1804 | Avg Loss: 0.1804 | LR: 0.000010
+[2026-01-09 19:28:40] Epoch: 97 | Iter: 000391 | Step Time: 3.531s | Local Loss: 0.0578 | Avg Loss: 0.0578 | LR: 0.000010
+[2026-01-09 19:28:46] Epoch: 98 | Iter: 000392 | Step Time: 3.580s | Local Loss: 0.0445 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-09 19:28:50] Epoch: 98 | Iter: 000393 | Step Time: 3.520s | Local Loss: 0.1439 | Avg Loss: 0.1439 | LR: 0.000010
+[2026-01-09 19:28:53] Epoch: 98 | Iter: 000394 | Step Time: 3.550s | Local Loss: 0.0719 | Avg Loss: 0.0719 | LR: 0.000010
+[2026-01-09 19:28:57] Epoch: 98 | Iter: 000395 | Step Time: 3.534s | Local Loss: 0.0271 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-09 19:29:03] Epoch: 99 | Iter: 000396 | Step Time: 3.864s | Local Loss: 0.2128 | Avg Loss: 0.2128 | LR: 0.000010
+[2026-01-09 19:29:07] Epoch: 99 | Iter: 000397 | Step Time: 3.538s | Local Loss: 0.5117 | Avg Loss: 0.5117 | LR: 0.000010
+[2026-01-09 19:29:10] Epoch: 99 | Iter: 000398 | Step Time: 3.512s | Local Loss: 0.3361 | Avg Loss: 0.3361 | LR: 0.000010
+[2026-01-09 19:29:14] Epoch: 99 | Iter: 000399 | Step Time: 3.542s | Local Loss: 0.1423 | Avg Loss: 0.1423 | LR: 0.000010
+[2026-01-09 19:29:20] Epoch: 100 | Iter: 000400 | Step Time: 3.559s | Local Loss: 0.1278 | Avg Loss: 0.1278 | LR: 0.000010
+[2026-01-09 19:29:24] Epoch: 100 | Iter: 000401 | Step Time: 3.522s | Local Loss: 0.0321 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-09 19:29:27] Epoch: 100 | Iter: 000402 | Step Time: 3.493s | Local Loss: 0.3798 | Avg Loss: 0.3798 | LR: 0.000010
+[2026-01-09 19:29:31] Epoch: 100 | Iter: 000403 | Step Time: 3.565s | Local Loss: 0.4954 | Avg Loss: 0.4954 | LR: 0.000010
+[2026-01-09 19:29:38] Epoch: 101 | Iter: 000404 | Step Time: 3.837s | Local Loss: 0.1836 | Avg Loss: 0.1836 | LR: 0.000010
+[2026-01-09 19:29:41] Epoch: 101 | Iter: 000405 | Step Time: 3.648s | Local Loss: 0.1775 | Avg Loss: 0.1775 | LR: 0.000010
+[2026-01-09 19:29:45] Epoch: 101 | Iter: 000406 | Step Time: 3.631s | Local Loss: 0.0915 | Avg Loss: 0.0915 | LR: 0.000010
+[2026-01-09 19:29:48] Epoch: 101 | Iter: 000407 | Step Time: 3.543s | Local Loss: 0.1071 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-09 19:29:54] Epoch: 102 | Iter: 000408 | Step Time: 3.535s | Local Loss: 0.2254 | Avg Loss: 0.2254 | LR: 0.000010
+[2026-01-09 19:29:58] Epoch: 102 | Iter: 000409 | Step Time: 3.616s | Local Loss: 0.1641 | Avg Loss: 0.1641 | LR: 0.000010
+[2026-01-09 19:30:02] Epoch: 102 | Iter: 000410 | Step Time: 3.510s | Local Loss: 0.0529 | Avg Loss: 0.0529 | LR: 0.000010
+[2026-01-09 19:30:06] Epoch: 102 | Iter: 000411 | Step Time: 4.370s | Local Loss: 0.1778 | Avg Loss: 0.1778 | LR: 0.000010
+[2026-01-09 19:30:12] Epoch: 103 | Iter: 000412 | Step Time: 3.588s | Local Loss: 0.0553 | Avg Loss: 0.0553 | LR: 0.000010
+[2026-01-09 19:30:15] Epoch: 103 | Iter: 000413 | Step Time: 3.494s | Local Loss: 0.4421 | Avg Loss: 0.4421 | LR: 0.000010
+[2026-01-09 19:30:19] Epoch: 103 | Iter: 000414 | Step Time: 3.531s | Local Loss: 0.4765 | Avg Loss: 0.4765 | LR: 0.000010
+[2026-01-09 19:30:23] Epoch: 103 | Iter: 000415 | Step Time: 3.515s | Local Loss: 0.1225 | Avg Loss: 0.1225 | LR: 0.000010
+[2026-01-09 19:30:29] Epoch: 104 | Iter: 000416 | Step Time: 3.885s | Local Loss: 0.4005 | Avg Loss: 0.4005 | LR: 0.000010
+[2026-01-09 19:30:33] Epoch: 104 | Iter: 000417 | Step Time: 3.517s | Local Loss: 0.1387 | Avg Loss: 0.1387 | LR: 0.000010
+[2026-01-09 19:30:37] Epoch: 104 | Iter: 000418 | Step Time: 3.571s | Local Loss: 0.4213 | Avg Loss: 0.4213 | LR: 0.000010
+[2026-01-09 19:30:40] Epoch: 104 | Iter: 000419 | Step Time: 3.559s | Local Loss: 0.1803 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-09 19:30:46] Epoch: 105 | Iter: 000420 | Step Time: 3.583s | Local Loss: 0.0940 | Avg Loss: 0.0940 | LR: 0.000010
+[2026-01-09 19:30:50] Epoch: 105 | Iter: 000421 | Step Time: 3.536s | Local Loss: 0.0246 | Avg Loss: 0.0246 | LR: 0.000010
+[2026-01-09 19:30:53] Epoch: 105 | Iter: 000422 | Step Time: 3.549s | Local Loss: 0.0187 | Avg Loss: 0.0187 | LR: 0.000010
+[2026-01-09 19:30:57] Epoch: 105 | Iter: 000423 | Step Time: 3.647s | Local Loss: 0.0584 | Avg Loss: 0.0584 | LR: 0.000010
+[2026-01-09 19:31:03] Epoch: 106 | Iter: 000424 | Step Time: 3.775s | Local Loss: 0.0387 | Avg Loss: 0.0387 | LR: 0.000010
+[2026-01-09 19:31:07] Epoch: 106 | Iter: 000425 | Step Time: 3.513s | Local Loss: 0.2269 | Avg Loss: 0.2269 | LR: 0.000010
+[2026-01-09 19:31:10] Epoch: 106 | Iter: 000426 | Step Time: 3.543s | Local Loss: 0.1249 | Avg Loss: 0.1249 | LR: 0.000010
+[2026-01-09 19:31:14] Epoch: 106 | Iter: 000427 | Step Time: 3.523s | Local Loss: 0.1071 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-09 19:31:20] Epoch: 107 | Iter: 000428 | Step Time: 3.553s | Local Loss: 0.1956 | Avg Loss: 0.1956 | LR: 0.000010
+[2026-01-09 19:31:24] Epoch: 107 | Iter: 000429 | Step Time: 3.535s | Local Loss: 0.0913 | Avg Loss: 0.0913 | LR: 0.000010
+[2026-01-09 19:31:27] Epoch: 107 | Iter: 000430 | Step Time: 3.499s | Local Loss: 0.1037 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-09 19:31:31] Epoch: 107 | Iter: 000431 | Step Time: 4.130s | Local Loss: 0.1487 | Avg Loss: 0.1487 | LR: 0.000010
+[2026-01-09 19:31:37] Epoch: 108 | Iter: 000432 | Step Time: 3.721s | Local Loss: 0.4175 | Avg Loss: 0.4175 | LR: 0.000010
+[2026-01-09 19:31:41] Epoch: 108 | Iter: 000433 | Step Time: 3.720s | Local Loss: 0.5735 | Avg Loss: 0.5735 | LR: 0.000010
+[2026-01-09 19:31:45] Epoch: 108 | Iter: 000434 | Step Time: 3.565s | Local Loss: 0.1272 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-09 19:31:48] Epoch: 108 | Iter: 000435 | Step Time: 3.571s | Local Loss: 0.1290 | Avg Loss: 0.1290 | LR: 0.000010
+[2026-01-09 19:31:54] Epoch: 109 | Iter: 000436 | Step Time: 3.554s | Local Loss: 0.0703 | Avg Loss: 0.0703 | LR: 0.000010
+[2026-01-09 19:31:58] Epoch: 109 | Iter: 000437 | Step Time: 3.498s | Local Loss: 0.5020 | Avg Loss: 0.5020 | LR: 0.000010
+[2026-01-09 19:32:01] Epoch: 109 | Iter: 000438 | Step Time: 3.504s | Local Loss: 0.0938 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-09 19:32:05] Epoch: 109 | Iter: 000439 | Step Time: 3.543s | Local Loss: 0.0353 | Avg Loss: 0.0353 | LR: 0.000010
+[2026-01-09 19:32:11] Epoch: 110 | Iter: 000440 | Step Time: 3.569s | Local Loss: 0.3357 | Avg Loss: 0.3357 | LR: 0.000010
+[2026-01-09 19:32:15] Epoch: 110 | Iter: 000441 | Step Time: 3.508s | Local Loss: 0.3096 | Avg Loss: 0.3096 | LR: 0.000010
+[2026-01-09 19:32:18] Epoch: 110 | Iter: 000442 | Step Time: 3.550s | Local Loss: 0.4676 | Avg Loss: 0.4676 | LR: 0.000010
+[2026-01-09 19:32:22] Epoch: 110 | Iter: 000443 | Step Time: 3.504s | Local Loss: 0.1065 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-09 19:32:28] Epoch: 111 | Iter: 000444 | Step Time: 3.858s | Local Loss: 0.0789 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-09 19:32:32] Epoch: 111 | Iter: 000445 | Step Time: 3.592s | Local Loss: 0.3375 | Avg Loss: 0.3375 | LR: 0.000010
+[2026-01-09 19:32:35] Epoch: 111 | Iter: 000446 | Step Time: 3.505s | Local Loss: 0.2636 | Avg Loss: 0.2636 | LR: 0.000010
+[2026-01-09 19:32:39] Epoch: 111 | Iter: 000447 | Step Time: 3.505s | Local Loss: 0.4987 | Avg Loss: 0.4987 | LR: 0.000010
+[2026-01-09 19:32:45] Epoch: 112 | Iter: 000448 | Step Time: 4.177s | Local Loss: 0.2287 | Avg Loss: 0.2287 | LR: 0.000010
+[2026-01-09 19:32:49] Epoch: 112 | Iter: 000449 | Step Time: 3.526s | Local Loss: 0.0890 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-09 19:33:54] Epoch: 112 | Iter: 000450 | Step Time: 3.468s | Local Loss: 0.2335 | Avg Loss: 0.2335 | LR: 0.000010
+[2026-01-09 19:33:57] Epoch: 112 | Iter: 000451 | Step Time: 3.526s | Local Loss: 0.0749 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-09 19:34:05] Epoch: 113 | Iter: 000452 | Step Time: 3.568s | Local Loss: 0.1414 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-09 19:34:08] Epoch: 113 | Iter: 000453 | Step Time: 3.461s | Local Loss: 0.1392 | Avg Loss: 0.1392 | LR: 0.000010
+[2026-01-09 19:34:12] Epoch: 113 | Iter: 000454 | Step Time: 3.431s | Local Loss: 0.3050 | Avg Loss: 0.3050 | LR: 0.000010
+[2026-01-09 19:34:15] Epoch: 113 | Iter: 000455 | Step Time: 3.408s | Local Loss: 0.2555 | Avg Loss: 0.2555 | LR: 0.000010
+[2026-01-09 19:34:23] Epoch: 114 | Iter: 000456 | Step Time: 3.577s | Local Loss: 0.2521 | Avg Loss: 0.2521 | LR: 0.000010
+[2026-01-09 19:34:26] Epoch: 114 | Iter: 000457 | Step Time: 3.445s | Local Loss: 0.1687 | Avg Loss: 0.1687 | LR: 0.000010
+[2026-01-09 19:34:30] Epoch: 114 | Iter: 000458 | Step Time: 3.485s | Local Loss: 0.0509 | Avg Loss: 0.0509 | LR: 0.000010
+[2026-01-09 19:34:33] Epoch: 114 | Iter: 000459 | Step Time: 3.604s | Local Loss: 0.4409 | Avg Loss: 0.4409 | LR: 0.000010
+[2026-01-09 19:34:40] Epoch: 115 | Iter: 000460 | Step Time: 3.577s | Local Loss: 0.0910 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-09 19:34:44] Epoch: 115 | Iter: 000461 | Step Time: 3.471s | Local Loss: 0.0471 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-09 19:34:48] Epoch: 115 | Iter: 000462 | Step Time: 3.452s | Local Loss: 0.2964 | Avg Loss: 0.2964 | LR: 0.000010
+[2026-01-09 19:34:51] Epoch: 115 | Iter: 000463 | Step Time: 3.473s | Local Loss: 0.1681 | Avg Loss: 0.1681 | LR: 0.000010
+[2026-01-09 19:34:59] Epoch: 116 | Iter: 000464 | Step Time: 3.819s | Local Loss: 0.0653 | Avg Loss: 0.0653 | LR: 0.000010
+[2026-01-09 19:35:03] Epoch: 116 | Iter: 000465 | Step Time: 3.433s | Local Loss: 0.1276 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-09 19:35:06] Epoch: 116 | Iter: 000466 | Step Time: 3.426s | Local Loss: 0.4547 | Avg Loss: 0.4547 | LR: 0.000010
+[2026-01-09 19:35:10] Epoch: 116 | Iter: 000467 | Step Time: 3.478s | Local Loss: 0.1123 | Avg Loss: 0.1123 | LR: 0.000010
+[2026-01-09 19:35:17] Epoch: 117 | Iter: 000468 | Step Time: 3.585s | Local Loss: 0.0435 | Avg Loss: 0.0435 | LR: 0.000010
+[2026-01-09 19:35:21] Epoch: 117 | Iter: 000469 | Step Time: 3.708s | Local Loss: 0.1063 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-09 19:35:24] Epoch: 117 | Iter: 000470 | Step Time: 3.468s | Local Loss: 0.4580 | Avg Loss: 0.4580 | LR: 0.000010
+[2026-01-09 19:35:28] Epoch: 117 | Iter: 000471 | Step Time: 3.463s | Local Loss: 0.1133 | Avg Loss: 0.1133 | LR: 0.000010
+[2026-01-09 19:35:35] Epoch: 118 | Iter: 000472 | Step Time: 3.610s | Local Loss: 0.0918 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-09 19:35:39] Epoch: 118 | Iter: 000473 | Step Time: 3.410s | Local Loss: 0.4479 | Avg Loss: 0.4479 | LR: 0.000010
+[2026-01-09 19:35:43] Epoch: 118 | Iter: 000474 | Step Time: 3.437s | Local Loss: 0.0809 | Avg Loss: 0.0809 | LR: 0.000010
+[2026-01-09 19:35:46] Epoch: 118 | Iter: 000475 | Step Time: 3.440s | Local Loss: 0.4472 | Avg Loss: 0.4472 | LR: 0.000010
+[2026-01-09 19:35:53] Epoch: 119 | Iter: 000476 | Step Time: 3.558s | Local Loss: 0.2881 | Avg Loss: 0.2881 | LR: 0.000010
+[2026-01-09 19:35:58] Epoch: 119 | Iter: 000477 | Step Time: 3.821s | Local Loss: 0.5757 | Avg Loss: 0.5757 | LR: 0.000010
+[2026-01-09 19:36:01] Epoch: 119 | Iter: 000478 | Step Time: 3.446s | Local Loss: 0.0493 | Avg Loss: 0.0493 | LR: 0.000010
+[2026-01-09 19:36:05] Epoch: 119 | Iter: 000479 | Step Time: 3.453s | Local Loss: 0.4129 | Avg Loss: 0.4129 | LR: 0.000010
+[2026-01-09 19:36:12] Epoch: 120 | Iter: 000480 | Step Time: 3.518s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 19:36:15] Epoch: 120 | Iter: 000481 | Step Time: 3.408s | Local Loss: 0.0906 | Avg Loss: 0.0906 | LR: 0.000010
+[2026-01-09 19:36:19] Epoch: 120 | Iter: 000482 | Step Time: 3.405s | Local Loss: 0.1198 | Avg Loss: 0.1198 | LR: 0.000010
+[2026-01-09 19:36:23] Epoch: 120 | Iter: 000483 | Step Time: 3.440s | Local Loss: 0.0785 | Avg Loss: 0.0785 | LR: 0.000010
+[2026-01-09 19:36:30] Epoch: 121 | Iter: 000484 | Step Time: 3.583s | Local Loss: 0.3078 | Avg Loss: 0.3078 | LR: 0.000010
+[2026-01-09 19:36:34] Epoch: 121 | Iter: 000485 | Step Time: 3.631s | Local Loss: 0.1413 | Avg Loss: 0.1413 | LR: 0.000010
+[2026-01-09 19:36:37] Epoch: 121 | Iter: 000486 | Step Time: 3.585s | Local Loss: 0.2903 | Avg Loss: 0.2903 | LR: 0.000010
+[2026-01-09 19:36:41] Epoch: 121 | Iter: 000487 | Step Time: 3.546s | Local Loss: 0.1143 | Avg Loss: 0.1143 | LR: 0.000010
+[2026-01-09 19:36:49] Epoch: 122 | Iter: 000488 | Step Time: 3.576s | Local Loss: 0.4657 | Avg Loss: 0.4657 | LR: 0.000010
+[2026-01-09 19:36:52] Epoch: 122 | Iter: 000489 | Step Time: 3.450s | Local Loss: 0.3358 | Avg Loss: 0.3358 | LR: 0.000010
+[2026-01-09 19:36:56] Epoch: 122 | Iter: 000490 | Step Time: 3.444s | Local Loss: 0.0721 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-09 19:36:59] Epoch: 122 | Iter: 000491 | Step Time: 3.443s | Local Loss: 0.0164 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-09 19:37:07] Epoch: 123 | Iter: 000492 | Step Time: 3.564s | Local Loss: 0.1255 | Avg Loss: 0.1255 | LR: 0.000010
+[2026-01-09 19:37:10] Epoch: 123 | Iter: 000493 | Step Time: 3.404s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 19:37:14] Epoch: 123 | Iter: 000494 | Step Time: 3.468s | Local Loss: 0.0720 | Avg Loss: 0.0720 | LR: 0.000010
+[2026-01-09 19:37:17] Epoch: 123 | Iter: 000495 | Step Time: 3.447s | Local Loss: 0.5932 | Avg Loss: 0.5932 | LR: 0.000010
+[2026-01-09 19:37:24] Epoch: 124 | Iter: 000496 | Step Time: 3.604s | Local Loss: 0.4184 | Avg Loss: 0.4184 | LR: 0.000010
+[2026-01-09 19:37:28] Epoch: 124 | Iter: 000497 | Step Time: 3.406s | Local Loss: 0.0501 | Avg Loss: 0.0501 | LR: 0.000010
+[2026-01-09 19:37:32] Epoch: 124 | Iter: 000498 | Step Time: 3.425s | Local Loss: 0.5042 | Avg Loss: 0.5042 | LR: 0.000010
+[2026-01-09 19:37:35] Epoch: 124 | Iter: 000499 | Step Time: 3.445s | Local Loss: 0.1796 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-09 19:37:43] Epoch: 125 | Iter: 000500 | Step Time: 3.681s | Local Loss: 0.4038 | Avg Loss: 0.4038 | LR: 0.000010
+[2026-01-09 19:37:47] Epoch: 125 | Iter: 000501 | Step Time: 3.659s | Local Loss: 0.1233 | Avg Loss: 0.1233 | LR: 0.000010
+[2026-01-09 19:37:50] Epoch: 125 | Iter: 000502 | Step Time: 3.437s | Local Loss: 0.1512 | Avg Loss: 0.1512 | LR: 0.000010
+[2026-01-09 19:37:54] Epoch: 125 | Iter: 000503 | Step Time: 3.446s | Local Loss: 0.1124 | Avg Loss: 0.1124 | LR: 0.000010
+[2026-01-09 19:38:01] Epoch: 126 | Iter: 000504 | Step Time: 3.534s | Local Loss: 0.1382 | Avg Loss: 0.1382 | LR: 0.000010
+[2026-01-09 19:38:05] Epoch: 126 | Iter: 000505 | Step Time: 3.812s | Local Loss: 0.0701 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-09 19:38:09] Epoch: 126 | Iter: 000506 | Step Time: 3.492s | Local Loss: 0.1509 | Avg Loss: 0.1509 | LR: 0.000010
+[2026-01-09 19:38:12] Epoch: 126 | Iter: 000507 | Step Time: 3.450s | Local Loss: 0.1138 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-09 19:38:20] Epoch: 127 | Iter: 000508 | Step Time: 3.516s | Local Loss: 0.0932 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-09 19:38:23] Epoch: 127 | Iter: 000509 | Step Time: 3.427s | Local Loss: 0.0618 | Avg Loss: 0.0618 | LR: 0.000010
+[2026-01-09 19:38:27] Epoch: 127 | Iter: 000510 | Step Time: 3.429s | Local Loss: 0.0471 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-09 19:38:30] Epoch: 127 | Iter: 000511 | Step Time: 3.438s | Local Loss: 0.3028 | Avg Loss: 0.3028 | LR: 0.000010
+[2026-01-09 19:38:38] Epoch: 128 | Iter: 000512 | Step Time: 3.531s | Local Loss: 0.3101 | Avg Loss: 0.3101 | LR: 0.000010
+[2026-01-09 19:38:41] Epoch: 128 | Iter: 000513 | Step Time: 3.450s | Local Loss: 0.3011 | Avg Loss: 0.3011 | LR: 0.000010
+[2026-01-09 19:38:44] Epoch: 128 | Iter: 000514 | Step Time: 3.453s | Local Loss: 0.0851 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-09 19:38:48] Epoch: 128 | Iter: 000515 | Step Time: 3.496s | Local Loss: 0.0821 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-09 19:38:56] Epoch: 129 | Iter: 000516 | Step Time: 3.974s | Local Loss: 0.2845 | Avg Loss: 0.2845 | LR: 0.000010
+[2026-01-09 19:38:59] Epoch: 129 | Iter: 000517 | Step Time: 3.468s | Local Loss: 0.0883 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-09 19:39:03] Epoch: 129 | Iter: 000518 | Step Time: 3.403s | Local Loss: 0.2515 | Avg Loss: 0.2515 | LR: 0.000010
+[2026-01-09 19:39:06] Epoch: 129 | Iter: 000519 | Step Time: 3.467s | Local Loss: 0.1709 | Avg Loss: 0.1709 | LR: 0.000010
+[2026-01-09 19:39:14] Epoch: 130 | Iter: 000520 | Step Time: 3.562s | Local Loss: 0.2166 | Avg Loss: 0.2166 | LR: 0.000010
+[2026-01-09 19:39:18] Epoch: 130 | Iter: 000521 | Step Time: 3.788s | Local Loss: 0.0917 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-09 19:39:21] Epoch: 130 | Iter: 000522 | Step Time: 3.446s | Local Loss: 0.0656 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-09 19:39:25] Epoch: 130 | Iter: 000523 | Step Time: 3.708s | Local Loss: 0.0753 | Avg Loss: 0.0753 | LR: 0.000010
+[2026-01-09 19:39:33] Epoch: 131 | Iter: 000524 | Step Time: 3.533s | Local Loss: 0.1148 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-09 19:39:36] Epoch: 131 | Iter: 000525 | Step Time: 3.414s | Local Loss: 0.1579 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-09 19:39:39] Epoch: 131 | Iter: 000526 | Step Time: 3.458s | Local Loss: 0.1253 | Avg Loss: 0.1253 | LR: 0.000010
+[2026-01-09 19:39:43] Epoch: 131 | Iter: 000527 | Step Time: 3.466s | Local Loss: 0.2010 | Avg Loss: 0.2010 | LR: 0.000010
+[2026-01-09 19:39:51] Epoch: 132 | Iter: 000528 | Step Time: 4.280s | Local Loss: 0.0614 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-09 19:39:55] Epoch: 132 | Iter: 000529 | Step Time: 3.780s | Local Loss: 0.3314 | Avg Loss: 0.3314 | LR: 0.000010
+[2026-01-09 19:39:59] Epoch: 132 | Iter: 000530 | Step Time: 3.464s | Local Loss: 0.3178 | Avg Loss: 0.3178 | LR: 0.000010
+[2026-01-09 19:40:03] Epoch: 132 | Iter: 000531 | Step Time: 3.576s | Local Loss: 0.0684 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-09 19:40:11] Epoch: 133 | Iter: 000532 | Step Time: 3.902s | Local Loss: 0.3036 | Avg Loss: 0.3036 | LR: 0.000010
+[2026-01-09 19:40:14] Epoch: 133 | Iter: 000533 | Step Time: 3.456s | Local Loss: 0.0748 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-09 19:40:18] Epoch: 133 | Iter: 000534 | Step Time: 3.457s | Local Loss: 0.4745 | Avg Loss: 0.4745 | LR: 0.000010
+[2026-01-09 19:40:21] Epoch: 133 | Iter: 000535 | Step Time: 3.581s | Local Loss: 0.3225 | Avg Loss: 0.3225 | LR: 0.000010
+[2026-01-09 19:40:29] Epoch: 134 | Iter: 000536 | Step Time: 3.942s | Local Loss: 0.0755 | Avg Loss: 0.0755 | LR: 0.000010
+[2026-01-09 19:40:33] Epoch: 134 | Iter: 000537 | Step Time: 3.478s | Local Loss: 0.3323 | Avg Loss: 0.3323 | LR: 0.000010
+[2026-01-09 19:40:36] Epoch: 134 | Iter: 000538 | Step Time: 3.585s | Local Loss: 0.1348 | Avg Loss: 0.1348 | LR: 0.000010
+[2026-01-09 19:40:40] Epoch: 134 | Iter: 000539 | Step Time: 3.504s | Local Loss: 0.0148 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-09 19:40:47] Epoch: 135 | Iter: 000540 | Step Time: 3.522s | Local Loss: 0.1059 | Avg Loss: 0.1059 | LR: 0.000010
+[2026-01-09 19:40:51] Epoch: 135 | Iter: 000541 | Step Time: 3.458s | Local Loss: 0.0190 | Avg Loss: 0.0190 | LR: 0.000010
+[2026-01-09 19:40:55] Epoch: 135 | Iter: 000542 | Step Time: 3.635s | Local Loss: 0.1682 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-09 19:40:58] Epoch: 135 | Iter: 000543 | Step Time: 3.477s | Local Loss: 0.1538 | Avg Loss: 0.1538 | LR: 0.000010
+[2026-01-09 19:41:06] Epoch: 136 | Iter: 000544 | Step Time: 3.531s | Local Loss: 0.0698 | Avg Loss: 0.0698 | LR: 0.000010
+[2026-01-09 19:41:09] Epoch: 136 | Iter: 000545 | Step Time: 3.427s | Local Loss: 0.3008 | Avg Loss: 0.3008 | LR: 0.000010
+[2026-01-09 19:41:13] Epoch: 136 | Iter: 000546 | Step Time: 3.398s | Local Loss: 0.5753 | Avg Loss: 0.5753 | LR: 0.000010
+[2026-01-09 19:41:16] Epoch: 136 | Iter: 000547 | Step Time: 3.427s | Local Loss: 0.0876 | Avg Loss: 0.0876 | LR: 0.000010
+[2026-01-09 19:41:24] Epoch: 137 | Iter: 000548 | Step Time: 3.913s | Local Loss: 0.0692 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-09 19:41:27] Epoch: 137 | Iter: 000549 | Step Time: 3.422s | Local Loss: 0.0839 | Avg Loss: 0.0839 | LR: 0.000010
+[2026-01-09 19:41:31] Epoch: 137 | Iter: 000550 | Step Time: 3.453s | Local Loss: 0.2469 | Avg Loss: 0.2469 | LR: 0.000010
+[2026-01-09 19:41:35] Epoch: 137 | Iter: 000551 | Step Time: 3.459s | Local Loss: 0.0188 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-09 19:41:42] Epoch: 138 | Iter: 000552 | Step Time: 3.902s | Local Loss: 0.1738 | Avg Loss: 0.1738 | LR: 0.000010
+[2026-01-09 19:41:46] Epoch: 138 | Iter: 000553 | Step Time: 3.699s | Local Loss: 0.3260 | Avg Loss: 0.3260 | LR: 0.000010
+[2026-01-09 19:41:50] Epoch: 138 | Iter: 000554 | Step Time: 3.444s | Local Loss: 0.2504 | Avg Loss: 0.2504 | LR: 0.000010
+[2026-01-09 19:41:53] Epoch: 138 | Iter: 000555 | Step Time: 3.450s | Local Loss: 0.0752 | Avg Loss: 0.0752 | LR: 0.000010
+[2026-01-09 19:42:01] Epoch: 139 | Iter: 000556 | Step Time: 3.584s | Local Loss: 0.3224 | Avg Loss: 0.3224 | LR: 0.000010
+[2026-01-09 19:42:05] Epoch: 139 | Iter: 000557 | Step Time: 3.590s | Local Loss: 0.1626 | Avg Loss: 0.1626 | LR: 0.000010
+[2026-01-09 19:42:08] Epoch: 139 | Iter: 000558 | Step Time: 3.455s | Local Loss: 0.1067 | Avg Loss: 0.1067 | LR: 0.000010
+[2026-01-09 19:42:12] Epoch: 139 | Iter: 000559 | Step Time: 3.516s | Local Loss: 0.0323 | Avg Loss: 0.0323 | LR: 0.000010
+[2026-01-09 19:42:19] Epoch: 140 | Iter: 000560 | Step Time: 3.559s | Local Loss: 0.0079 | Avg Loss: 0.0079 | LR: 0.000010
+[2026-01-09 19:42:23] Epoch: 140 | Iter: 000561 | Step Time: 3.487s | Local Loss: 0.1802 | Avg Loss: 0.1802 | LR: 0.000010
+[2026-01-09 19:42:26] Epoch: 140 | Iter: 000562 | Step Time: 3.459s | Local Loss: 0.3750 | Avg Loss: 0.3750 | LR: 0.000010
+[2026-01-09 19:42:30] Epoch: 140 | Iter: 000563 | Step Time: 3.436s | Local Loss: 0.0909 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-09 19:42:37] Epoch: 141 | Iter: 000564 | Step Time: 3.533s | Local Loss: 0.0952 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-09 19:42:41] Epoch: 141 | Iter: 000565 | Step Time: 3.426s | Local Loss: 0.4023 | Avg Loss: 0.4023 | LR: 0.000010
+[2026-01-09 19:42:44] Epoch: 141 | Iter: 000566 | Step Time: 3.395s | Local Loss: 0.1877 | Avg Loss: 0.1877 | LR: 0.000010
+[2026-01-09 19:42:48] Epoch: 141 | Iter: 000567 | Step Time: 3.477s | Local Loss: 0.1618 | Avg Loss: 0.1618 | LR: 0.000010
+[2026-01-09 19:42:55] Epoch: 142 | Iter: 000568 | Step Time: 3.682s | Local Loss: 0.0146 | Avg Loss: 0.0146 | LR: 0.000010
+[2026-01-09 19:42:59] Epoch: 142 | Iter: 000569 | Step Time: 3.805s | Local Loss: 0.2336 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-09 19:43:03] Epoch: 142 | Iter: 000570 | Step Time: 3.438s | Local Loss: 0.0287 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-09 19:43:06] Epoch: 142 | Iter: 000571 | Step Time: 3.409s | Local Loss: 0.0957 | Avg Loss: 0.0957 | LR: 0.000010
+[2026-01-09 19:43:14] Epoch: 143 | Iter: 000572 | Step Time: 3.561s | Local Loss: 0.4906 | Avg Loss: 0.4906 | LR: 0.000010
+[2026-01-09 19:43:17] Epoch: 143 | Iter: 000573 | Step Time: 3.411s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 19:43:21] Epoch: 143 | Iter: 000574 | Step Time: 3.408s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 19:43:24] Epoch: 143 | Iter: 000575 | Step Time: 3.471s | Local Loss: 0.0650 | Avg Loss: 0.0650 | LR: 0.000010
+[2026-01-09 19:43:32] Epoch: 144 | Iter: 000576 | Step Time: 3.506s | Local Loss: 0.1036 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-09 19:43:35] Epoch: 144 | Iter: 000577 | Step Time: 3.418s | Local Loss: 0.1498 | Avg Loss: 0.1498 | LR: 0.000010
+[2026-01-09 19:43:39] Epoch: 144 | Iter: 000578 | Step Time: 3.396s | Local Loss: 0.1013 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-09 19:43:42] Epoch: 144 | Iter: 000579 | Step Time: 3.480s | Local Loss: 0.1053 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-09 19:43:50] Epoch: 145 | Iter: 000580 | Step Time: 3.558s | Local Loss: 0.1733 | Avg Loss: 0.1733 | LR: 0.000010
+[2026-01-09 19:43:53] Epoch: 145 | Iter: 000581 | Step Time: 3.437s | Local Loss: 0.0605 | Avg Loss: 0.0605 | LR: 0.000010
+[2026-01-09 19:43:57] Epoch: 145 | Iter: 000582 | Step Time: 3.441s | Local Loss: 0.1252 | Avg Loss: 0.1252 | LR: 0.000010
+[2026-01-09 19:44:00] Epoch: 145 | Iter: 000583 | Step Time: 3.410s | Local Loss: 0.4856 | Avg Loss: 0.4856 | LR: 0.000010
+[2026-01-09 19:44:07] Epoch: 146 | Iter: 000584 | Step Time: 3.577s | Local Loss: 0.1541 | Avg Loss: 0.1541 | LR: 0.000010
+[2026-01-09 19:44:12] Epoch: 146 | Iter: 000585 | Step Time: 3.895s | Local Loss: 0.3969 | Avg Loss: 0.3969 | LR: 0.000010
+[2026-01-09 19:44:15] Epoch: 146 | Iter: 000586 | Step Time: 3.441s | Local Loss: 0.0549 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-09 19:44:19] Epoch: 146 | Iter: 000587 | Step Time: 3.462s | Local Loss: 0.3415 | Avg Loss: 0.3415 | LR: 0.000010
+[2026-01-09 19:44:26] Epoch: 147 | Iter: 000588 | Step Time: 3.593s | Local Loss: 0.0739 | Avg Loss: 0.0739 | LR: 0.000010
+[2026-01-09 19:44:29] Epoch: 147 | Iter: 000589 | Step Time: 3.426s | Local Loss: 0.1546 | Avg Loss: 0.1546 | LR: 0.000010
+[2026-01-09 19:44:33] Epoch: 147 | Iter: 000590 | Step Time: 3.450s | Local Loss: 0.0266 | Avg Loss: 0.0266 | LR: 0.000010
+[2026-01-09 19:44:37] Epoch: 147 | Iter: 000591 | Step Time: 3.607s | Local Loss: 0.0134 | Avg Loss: 0.0134 | LR: 0.000010
+[2026-01-09 19:44:44] Epoch: 148 | Iter: 000592 | Step Time: 3.903s | Local Loss: 0.4606 | Avg Loss: 0.4606 | LR: 0.000010
+[2026-01-09 19:44:48] Epoch: 148 | Iter: 000593 | Step Time: 3.432s | Local Loss: 0.1614 | Avg Loss: 0.1614 | LR: 0.000010
+[2026-01-09 19:44:52] Epoch: 148 | Iter: 000594 | Step Time: 3.458s | Local Loss: 0.3094 | Avg Loss: 0.3094 | LR: 0.000010
+[2026-01-09 19:44:55] Epoch: 148 | Iter: 000595 | Step Time: 3.477s | Local Loss: 0.0204 | Avg Loss: 0.0204 | LR: 0.000010
+[2026-01-09 19:45:03] Epoch: 149 | Iter: 000596 | Step Time: 3.961s | Local Loss: 0.0197 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-09 19:45:07] Epoch: 149 | Iter: 000597 | Step Time: 3.453s | Local Loss: 0.1012 | Avg Loss: 0.1012 | LR: 0.000010
+[2026-01-09 19:45:10] Epoch: 149 | Iter: 000598 | Step Time: 3.432s | Local Loss: 0.0996 | Avg Loss: 0.0996 | LR: 0.000010
+[2026-01-09 19:45:14] Epoch: 149 | Iter: 000599 | Step Time: 3.442s | Local Loss: 0.5053 | Avg Loss: 0.5053 | LR: 0.000010
+[2026-01-09 19:46:15] Epoch: 150 | Iter: 000600 | Step Time: 3.558s | Local Loss: 0.0620 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-09 19:46:19] Epoch: 150 | Iter: 000601 | Step Time: 3.413s | Local Loss: 0.0614 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-09 19:46:22] Epoch: 150 | Iter: 000602 | Step Time: 3.421s | Local Loss: 0.1395 | Avg Loss: 0.1395 | LR: 0.000010
+[2026-01-09 19:46:26] Epoch: 150 | Iter: 000603 | Step Time: 3.409s | Local Loss: 0.0812 | Avg Loss: 0.0812 | LR: 0.000010
+[2026-01-09 19:46:34] Epoch: 151 | Iter: 000604 | Step Time: 3.517s | Local Loss: 0.2139 | Avg Loss: 0.2139 | LR: 0.000010
+[2026-01-09 19:46:37] Epoch: 151 | Iter: 000605 | Step Time: 3.417s | Local Loss: 0.5596 | Avg Loss: 0.5596 | LR: 0.000010
+[2026-01-09 19:46:41] Epoch: 151 | Iter: 000606 | Step Time: 3.472s | Local Loss: 0.0479 | Avg Loss: 0.0479 | LR: 0.000010
+[2026-01-09 19:46:44] Epoch: 151 | Iter: 000607 | Step Time: 3.437s | Local Loss: 0.0422 | Avg Loss: 0.0422 | LR: 0.000010
+[2026-01-09 19:46:52] Epoch: 152 | Iter: 000608 | Step Time: 3.525s | Local Loss: 0.0995 | Avg Loss: 0.0995 | LR: 0.000010
+[2026-01-09 19:46:56] Epoch: 152 | Iter: 000609 | Step Time: 3.404s | Local Loss: 0.0431 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-09 19:46:59] Epoch: 152 | Iter: 000610 | Step Time: 3.460s | Local Loss: 0.1680 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-09 19:47:03] Epoch: 152 | Iter: 000611 | Step Time: 3.434s | Local Loss: 0.2494 | Avg Loss: 0.2494 | LR: 0.000010
+[2026-01-09 19:47:11] Epoch: 153 | Iter: 000612 | Step Time: 3.597s | Local Loss: 0.2334 | Avg Loss: 0.2334 | LR: 0.000010
+[2026-01-09 19:47:15] Epoch: 153 | Iter: 000613 | Step Time: 3.586s | Local Loss: 0.1735 | Avg Loss: 0.1735 | LR: 0.000010
+[2026-01-09 19:47:18] Epoch: 153 | Iter: 000614 | Step Time: 3.444s | Local Loss: 0.4355 | Avg Loss: 0.4355 | LR: 0.000010
+[2026-01-09 19:47:22] Epoch: 153 | Iter: 000615 | Step Time: 3.449s | Local Loss: 0.3984 | Avg Loss: 0.3984 | LR: 0.000010
+[2026-01-09 19:47:30] Epoch: 154 | Iter: 000616 | Step Time: 3.607s | Local Loss: 0.0382 | Avg Loss: 0.0382 | LR: 0.000010
+[2026-01-09 19:47:34] Epoch: 154 | Iter: 000617 | Step Time: 3.795s | Local Loss: 0.3565 | Avg Loss: 0.3565 | LR: 0.000010
+[2026-01-09 19:47:37] Epoch: 154 | Iter: 000618 | Step Time: 3.432s | Local Loss: 0.4044 | Avg Loss: 0.4044 | LR: 0.000010
+[2026-01-09 19:47:41] Epoch: 154 | Iter: 000619 | Step Time: 3.440s | Local Loss: 0.4017 | Avg Loss: 0.4017 | LR: 0.000010
+[2026-01-09 19:47:49] Epoch: 155 | Iter: 000620 | Step Time: 3.569s | Local Loss: 0.1532 | Avg Loss: 0.1532 | LR: 0.000010
+[2026-01-09 19:47:52] Epoch: 155 | Iter: 000621 | Step Time: 3.414s | Local Loss: 0.0825 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-09 19:47:56] Epoch: 155 | Iter: 000622 | Step Time: 3.562s | Local Loss: 0.1030 | Avg Loss: 0.1030 | LR: 0.000010
+[2026-01-09 19:48:00] Epoch: 155 | Iter: 000623 | Step Time: 3.435s | Local Loss: 0.3762 | Avg Loss: 0.3762 | LR: 0.000010
+[2026-01-09 19:48:08] Epoch: 156 | Iter: 000624 | Step Time: 3.901s | Local Loss: 0.1460 | Avg Loss: 0.1460 | LR: 0.000010
+[2026-01-09 19:48:11] Epoch: 156 | Iter: 000625 | Step Time: 3.442s | Local Loss: 0.3926 | Avg Loss: 0.3926 | LR: 0.000010
+[2026-01-09 19:48:15] Epoch: 156 | Iter: 000626 | Step Time: 3.537s | Local Loss: 0.0651 | Avg Loss: 0.0651 | LR: 0.000010
+[2026-01-09 19:48:19] Epoch: 156 | Iter: 000627 | Step Time: 3.436s | Local Loss: 0.0791 | Avg Loss: 0.0791 | LR: 0.000010
+[2026-01-09 19:48:27] Epoch: 157 | Iter: 000628 | Step Time: 3.951s | Local Loss: 0.0319 | Avg Loss: 0.0319 | LR: 0.000010
+[2026-01-09 19:48:31] Epoch: 157 | Iter: 000629 | Step Time: 3.731s | Local Loss: 0.0943 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-09 19:48:34] Epoch: 157 | Iter: 000630 | Step Time: 3.445s | Local Loss: 0.0553 | Avg Loss: 0.0553 | LR: 0.000010
+[2026-01-09 19:48:38] Epoch: 157 | Iter: 000631 | Step Time: 3.445s | Local Loss: 0.0348 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-09 19:48:47] Epoch: 158 | Iter: 000632 | Step Time: 3.877s | Local Loss: 0.0746 | Avg Loss: 0.0746 | LR: 0.000010
+[2026-01-09 19:48:50] Epoch: 158 | Iter: 000633 | Step Time: 3.497s | Local Loss: 0.2780 | Avg Loss: 0.2780 | LR: 0.000010
+[2026-01-09 19:48:54] Epoch: 158 | Iter: 000634 | Step Time: 3.468s | Local Loss: 0.1471 | Avg Loss: 0.1471 | LR: 0.000010
+[2026-01-09 19:48:57] Epoch: 158 | Iter: 000635 | Step Time: 3.414s | Local Loss: 0.1314 | Avg Loss: 0.1314 | LR: 0.000010
+[2026-01-09 19:49:06] Epoch: 159 | Iter: 000636 | Step Time: 3.942s | Local Loss: 0.5455 | Avg Loss: 0.5455 | LR: 0.000010
+[2026-01-09 19:49:10] Epoch: 159 | Iter: 000637 | Step Time: 3.872s | Local Loss: 0.0649 | Avg Loss: 0.0649 | LR: 0.000010
+[2026-01-09 19:49:14] Epoch: 159 | Iter: 000638 | Step Time: 3.458s | Local Loss: 0.0478 | Avg Loss: 0.0478 | LR: 0.000010
+[2026-01-09 19:49:17] Epoch: 159 | Iter: 000639 | Step Time: 3.415s | Local Loss: 0.1208 | Avg Loss: 0.1208 | LR: 0.000010
+[2026-01-09 19:49:25] Epoch: 160 | Iter: 000640 | Step Time: 3.599s | Local Loss: 0.0781 | Avg Loss: 0.0781 | LR: 0.000010
+[2026-01-09 19:49:29] Epoch: 160 | Iter: 000641 | Step Time: 3.442s | Local Loss: 0.2213 | Avg Loss: 0.2213 | LR: 0.000010
+[2026-01-09 19:49:32] Epoch: 160 | Iter: 000642 | Step Time: 3.437s | Local Loss: 0.1163 | Avg Loss: 0.1163 | LR: 0.000010
+[2026-01-09 19:49:36] Epoch: 160 | Iter: 000643 | Step Time: 3.438s | Local Loss: 0.3523 | Avg Loss: 0.3523 | LR: 0.000010
+[2026-01-09 19:49:44] Epoch: 161 | Iter: 000644 | Step Time: 3.913s | Local Loss: 0.0573 | Avg Loss: 0.0573 | LR: 0.000010
+[2026-01-09 19:49:47] Epoch: 161 | Iter: 000645 | Step Time: 3.483s | Local Loss: 0.1131 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-09 19:49:51] Epoch: 161 | Iter: 000646 | Step Time: 3.458s | Local Loss: 0.0610 | Avg Loss: 0.0610 | LR: 0.000010
+[2026-01-09 19:49:55] Epoch: 161 | Iter: 000647 | Step Time: 3.412s | Local Loss: 0.1308 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-09 19:50:03] Epoch: 162 | Iter: 000648 | Step Time: 3.900s | Local Loss: 0.3724 | Avg Loss: 0.3724 | LR: 0.000010
+[2026-01-09 19:50:06] Epoch: 162 | Iter: 000649 | Step Time: 3.433s | Local Loss: 0.2514 | Avg Loss: 0.2514 | LR: 0.000010
+[2026-01-09 19:50:10] Epoch: 162 | Iter: 000650 | Step Time: 3.463s | Local Loss: 0.0071 | Avg Loss: 0.0071 | LR: 0.000010
+[2026-01-09 19:50:14] Epoch: 162 | Iter: 000651 | Step Time: 3.478s | Local Loss: 0.1260 | Avg Loss: 0.1260 | LR: 0.000010
+[2026-01-09 19:50:22] Epoch: 163 | Iter: 000652 | Step Time: 3.558s | Local Loss: 0.2211 | Avg Loss: 0.2211 | LR: 0.000010
+[2026-01-09 19:50:25] Epoch: 163 | Iter: 000653 | Step Time: 3.439s | Local Loss: 0.2792 | Avg Loss: 0.2792 | LR: 0.000010
+[2026-01-09 19:50:29] Epoch: 163 | Iter: 000654 | Step Time: 3.637s | Local Loss: 0.0942 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-09 19:50:33] Epoch: 163 | Iter: 000655 | Step Time: 3.474s | Local Loss: 0.0314 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-09 19:50:41] Epoch: 164 | Iter: 000656 | Step Time: 3.634s | Local Loss: 0.1272 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-09 19:50:45] Epoch: 164 | Iter: 000657 | Step Time: 3.798s | Local Loss: 0.4623 | Avg Loss: 0.4623 | LR: 0.000010
+[2026-01-09 19:50:48] Epoch: 164 | Iter: 000658 | Step Time: 3.407s | Local Loss: 0.0788 | Avg Loss: 0.0788 | LR: 0.000010
+[2026-01-09 19:50:52] Epoch: 164 | Iter: 000659 | Step Time: 3.440s | Local Loss: 0.3532 | Avg Loss: 0.3532 | LR: 0.000010
+[2026-01-09 19:51:00] Epoch: 165 | Iter: 000660 | Step Time: 3.910s | Local Loss: 0.0153 | Avg Loss: 0.0153 | LR: 0.000010
+[2026-01-09 19:51:04] Epoch: 165 | Iter: 000661 | Step Time: 3.734s | Local Loss: 0.4546 | Avg Loss: 0.4546 | LR: 0.000010
+[2026-01-09 19:51:07] Epoch: 165 | Iter: 000662 | Step Time: 3.447s | Local Loss: 0.0812 | Avg Loss: 0.0812 | LR: 0.000010
+[2026-01-09 19:51:11] Epoch: 165 | Iter: 000663 | Step Time: 3.443s | Local Loss: 0.3728 | Avg Loss: 0.3728 | LR: 0.000010
+[2026-01-09 19:51:19] Epoch: 166 | Iter: 000664 | Step Time: 3.894s | Local Loss: 0.1070 | Avg Loss: 0.1070 | LR: 0.000010
+[2026-01-09 19:51:23] Epoch: 166 | Iter: 000665 | Step Time: 3.730s | Local Loss: 0.0405 | Avg Loss: 0.0405 | LR: 0.000010
+[2026-01-09 19:51:26] Epoch: 166 | Iter: 000666 | Step Time: 3.401s | Local Loss: 0.0941 | Avg Loss: 0.0941 | LR: 0.000010
+[2026-01-09 19:51:30] Epoch: 166 | Iter: 000667 | Step Time: 3.442s | Local Loss: 0.1027 | Avg Loss: 0.1027 | LR: 0.000010
+[2026-01-09 19:51:38] Epoch: 167 | Iter: 000668 | Step Time: 3.524s | Local Loss: 0.5569 | Avg Loss: 0.5569 | LR: 0.000010
+[2026-01-09 19:51:42] Epoch: 167 | Iter: 000669 | Step Time: 3.856s | Local Loss: 0.4995 | Avg Loss: 0.4995 | LR: 0.000010
+[2026-01-09 19:51:46] Epoch: 167 | Iter: 000670 | Step Time: 3.442s | Local Loss: 0.0666 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-09 19:51:49] Epoch: 167 | Iter: 000671 | Step Time: 3.457s | Local Loss: 0.0653 | Avg Loss: 0.0653 | LR: 0.000010
+[2026-01-09 19:51:58] Epoch: 168 | Iter: 000672 | Step Time: 3.574s | Local Loss: 0.4075 | Avg Loss: 0.4075 | LR: 0.000010
+[2026-01-09 19:52:01] Epoch: 168 | Iter: 000673 | Step Time: 3.468s | Local Loss: 0.2468 | Avg Loss: 0.2468 | LR: 0.000010
+[2026-01-09 19:52:05] Epoch: 168 | Iter: 000674 | Step Time: 3.399s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 19:52:08] Epoch: 168 | Iter: 000675 | Step Time: 3.528s | Local Loss: 0.5979 | Avg Loss: 0.5979 | LR: 0.000010
+[2026-01-09 19:52:17] Epoch: 169 | Iter: 000676 | Step Time: 3.553s | Local Loss: 0.3084 | Avg Loss: 0.3084 | LR: 0.000010
+[2026-01-09 19:52:20] Epoch: 169 | Iter: 000677 | Step Time: 3.444s | Local Loss: 0.0446 | Avg Loss: 0.0446 | LR: 0.000010
+[2026-01-09 19:52:24] Epoch: 169 | Iter: 000678 | Step Time: 3.519s | Local Loss: 0.3827 | Avg Loss: 0.3827 | LR: 0.000010
+[2026-01-09 19:52:27] Epoch: 169 | Iter: 000679 | Step Time: 3.470s | Local Loss: 0.0974 | Avg Loss: 0.0974 | LR: 0.000010
+[2026-01-09 19:52:35] Epoch: 170 | Iter: 000680 | Step Time: 3.551s | Local Loss: 0.2213 | Avg Loss: 0.2213 | LR: 0.000010
+[2026-01-09 19:52:39] Epoch: 170 | Iter: 000681 | Step Time: 3.416s | Local Loss: 0.0488 | Avg Loss: 0.0488 | LR: 0.000010
+[2026-01-09 19:52:43] Epoch: 170 | Iter: 000682 | Step Time: 3.447s | Local Loss: 0.3609 | Avg Loss: 0.3609 | LR: 0.000010
+[2026-01-09 19:52:46] Epoch: 170 | Iter: 000683 | Step Time: 3.520s | Local Loss: 0.0547 | Avg Loss: 0.0547 | LR: 0.000010
+[2026-01-09 19:52:54] Epoch: 171 | Iter: 000684 | Step Time: 3.520s | Local Loss: 0.3844 | Avg Loss: 0.3844 | LR: 0.000010
+[2026-01-09 19:52:58] Epoch: 171 | Iter: 000685 | Step Time: 3.425s | Local Loss: 0.0407 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-09 19:53:01] Epoch: 171 | Iter: 000686 | Step Time: 3.451s | Local Loss: 0.0147 | Avg Loss: 0.0147 | LR: 0.000010
+[2026-01-09 19:53:05] Epoch: 171 | Iter: 000687 | Step Time: 3.437s | Local Loss: 0.2456 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-09 19:53:13] Epoch: 172 | Iter: 000688 | Step Time: 3.568s | Local Loss: 0.3018 | Avg Loss: 0.3018 | LR: 0.000010
+[2026-01-09 19:53:17] Epoch: 172 | Iter: 000689 | Step Time: 3.780s | Local Loss: 0.2402 | Avg Loss: 0.2402 | LR: 0.000010
+[2026-01-09 19:53:20] Epoch: 172 | Iter: 000690 | Step Time: 3.429s | Local Loss: 0.1689 | Avg Loss: 0.1689 | LR: 0.000010
+[2026-01-09 19:53:24] Epoch: 172 | Iter: 000691 | Step Time: 3.412s | Local Loss: 0.3128 | Avg Loss: 0.3128 | LR: 0.000010
+[2026-01-09 19:53:32] Epoch: 173 | Iter: 000692 | Step Time: 3.659s | Local Loss: 0.1561 | Avg Loss: 0.1561 | LR: 0.000010
+[2026-01-09 19:53:36] Epoch: 173 | Iter: 000693 | Step Time: 3.471s | Local Loss: 0.0365 | Avg Loss: 0.0365 | LR: 0.000010
+[2026-01-09 19:53:40] Epoch: 173 | Iter: 000694 | Step Time: 3.479s | Local Loss: 0.0304 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-09 19:53:43] Epoch: 173 | Iter: 000695 | Step Time: 3.437s | Local Loss: 0.3609 | Avg Loss: 0.3609 | LR: 0.000010
+[2026-01-09 19:53:51] Epoch: 174 | Iter: 000696 | Step Time: 3.540s | Local Loss: 0.3613 | Avg Loss: 0.3613 | LR: 0.000010
+[2026-01-09 19:53:55] Epoch: 174 | Iter: 000697 | Step Time: 3.828s | Local Loss: 0.0438 | Avg Loss: 0.0438 | LR: 0.000010
+[2026-01-09 19:53:59] Epoch: 174 | Iter: 000698 | Step Time: 3.472s | Local Loss: 0.1386 | Avg Loss: 0.1386 | LR: 0.000010
+[2026-01-09 19:54:03] Epoch: 174 | Iter: 000699 | Step Time: 3.430s | Local Loss: 0.3237 | Avg Loss: 0.3237 | LR: 0.000010
+[2026-01-09 19:54:11] Epoch: 175 | Iter: 000700 | Step Time: 3.550s | Local Loss: 0.0829 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-09 19:54:14] Epoch: 175 | Iter: 000701 | Step Time: 3.411s | Local Loss: 0.0361 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-09 19:54:18] Epoch: 175 | Iter: 000702 | Step Time: 3.466s | Local Loss: 0.2221 | Avg Loss: 0.2221 | LR: 0.000010
+[2026-01-09 19:54:21] Epoch: 175 | Iter: 000703 | Step Time: 3.443s | Local Loss: 0.1602 | Avg Loss: 0.1602 | LR: 0.000010
+[2026-01-09 19:54:29] Epoch: 176 | Iter: 000704 | Step Time: 3.546s | Local Loss: 0.0867 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-09 19:54:33] Epoch: 176 | Iter: 000705 | Step Time: 3.413s | Local Loss: 0.1075 | Avg Loss: 0.1075 | LR: 0.000010
+[2026-01-09 19:54:36] Epoch: 176 | Iter: 000706 | Step Time: 3.466s | Local Loss: 0.0697 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-09 19:54:40] Epoch: 176 | Iter: 000707 | Step Time: 3.519s | Local Loss: 0.2100 | Avg Loss: 0.2100 | LR: 0.000010
+[2026-01-09 19:54:48] Epoch: 177 | Iter: 000708 | Step Time: 3.616s | Local Loss: 0.3953 | Avg Loss: 0.3953 | LR: 0.000010
+[2026-01-09 19:54:52] Epoch: 177 | Iter: 000709 | Step Time: 3.497s | Local Loss: 0.2793 | Avg Loss: 0.2793 | LR: 0.000010
+[2026-01-09 19:54:55] Epoch: 177 | Iter: 000710 | Step Time: 3.401s | Local Loss: 0.4963 | Avg Loss: 0.4963 | LR: 0.000010
+[2026-01-09 19:54:59] Epoch: 177 | Iter: 000711 | Step Time: 3.465s | Local Loss: 0.0988 | Avg Loss: 0.0988 | LR: 0.000010
+[2026-01-09 19:55:07] Epoch: 178 | Iter: 000712 | Step Time: 3.531s | Local Loss: 0.4365 | Avg Loss: 0.4365 | LR: 0.000010
+[2026-01-09 19:55:10] Epoch: 178 | Iter: 000713 | Step Time: 3.449s | Local Loss: 0.3685 | Avg Loss: 0.3685 | LR: 0.000010
+[2026-01-09 19:55:14] Epoch: 178 | Iter: 000714 | Step Time: 3.454s | Local Loss: 0.0650 | Avg Loss: 0.0650 | LR: 0.000010
+[2026-01-09 19:55:17] Epoch: 178 | Iter: 000715 | Step Time: 3.473s | Local Loss: 0.2003 | Avg Loss: 0.2003 | LR: 0.000010
+[2026-01-09 19:55:25] Epoch: 179 | Iter: 000716 | Step Time: 3.552s | Local Loss: 0.1222 | Avg Loss: 0.1222 | LR: 0.000010
+[2026-01-09 19:55:29] Epoch: 179 | Iter: 000717 | Step Time: 3.456s | Local Loss: 0.1730 | Avg Loss: 0.1730 | LR: 0.000010
+[2026-01-09 19:55:32] Epoch: 179 | Iter: 000718 | Step Time: 3.458s | Local Loss: 0.5492 | Avg Loss: 0.5492 | LR: 0.000010
+[2026-01-09 19:55:36] Epoch: 179 | Iter: 000719 | Step Time: 3.453s | Local Loss: 0.2621 | Avg Loss: 0.2621 | LR: 0.000010
+[2026-01-09 19:55:44] Epoch: 180 | Iter: 000720 | Step Time: 3.528s | Local Loss: 0.0702 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-09 19:55:47] Epoch: 180 | Iter: 000721 | Step Time: 3.409s | Local Loss: 0.4713 | Avg Loss: 0.4713 | LR: 0.000010
+[2026-01-09 19:55:51] Epoch: 180 | Iter: 000722 | Step Time: 3.463s | Local Loss: 0.4998 | Avg Loss: 0.4998 | LR: 0.000010
+[2026-01-09 19:55:55] Epoch: 180 | Iter: 000723 | Step Time: 3.444s | Local Loss: 0.1683 | Avg Loss: 0.1683 | LR: 0.000010
+[2026-01-09 19:56:03] Epoch: 181 | Iter: 000724 | Step Time: 3.543s | Local Loss: 0.3479 | Avg Loss: 0.3479 | LR: 0.000010
+[2026-01-09 19:56:06] Epoch: 181 | Iter: 000725 | Step Time: 3.538s | Local Loss: 0.0080 | Avg Loss: 0.0080 | LR: 0.000010
+[2026-01-09 19:56:10] Epoch: 181 | Iter: 000726 | Step Time: 3.436s | Local Loss: 0.0273 | Avg Loss: 0.0273 | LR: 0.000010
+[2026-01-09 19:56:14] Epoch: 181 | Iter: 000727 | Step Time: 3.440s | Local Loss: 0.0548 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-09 19:56:22] Epoch: 182 | Iter: 000728 | Step Time: 3.527s | Local Loss: 0.1118 | Avg Loss: 0.1118 | LR: 0.000010
+[2026-01-09 19:56:25] Epoch: 182 | Iter: 000729 | Step Time: 3.432s | Local Loss: 0.0784 | Avg Loss: 0.0784 | LR: 0.000010
+[2026-01-09 19:56:29] Epoch: 182 | Iter: 000730 | Step Time: 3.430s | Local Loss: 0.2974 | Avg Loss: 0.2974 | LR: 0.000010
+[2026-01-09 19:56:33] Epoch: 182 | Iter: 000731 | Step Time: 3.432s | Local Loss: 0.0863 | Avg Loss: 0.0863 | LR: 0.000010
+[2026-01-09 19:56:41] Epoch: 183 | Iter: 000732 | Step Time: 3.516s | Local Loss: 0.0832 | Avg Loss: 0.0832 | LR: 0.000010
+[2026-01-09 19:56:44] Epoch: 183 | Iter: 000733 | Step Time: 3.493s | Local Loss: 0.3870 | Avg Loss: 0.3870 | LR: 0.000010
+[2026-01-09 19:56:48] Epoch: 183 | Iter: 000734 | Step Time: 3.663s | Local Loss: 0.2315 | Avg Loss: 0.2315 | LR: 0.000010
+[2026-01-09 19:56:52] Epoch: 183 | Iter: 000735 | Step Time: 3.448s | Local Loss: 0.4093 | Avg Loss: 0.4093 | LR: 0.000010
+[2026-01-09 19:56:59] Epoch: 184 | Iter: 000736 | Step Time: 3.516s | Local Loss: 0.0212 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-09 19:57:03] Epoch: 184 | Iter: 000737 | Step Time: 3.774s | Local Loss: 0.0887 | Avg Loss: 0.0887 | LR: 0.000010
+[2026-01-09 19:57:07] Epoch: 184 | Iter: 000738 | Step Time: 3.429s | Local Loss: 0.3629 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-09 19:57:11] Epoch: 184 | Iter: 000739 | Step Time: 3.563s | Local Loss: 0.0633 | Avg Loss: 0.0633 | LR: 0.000010
+[2026-01-09 19:57:19] Epoch: 185 | Iter: 000740 | Step Time: 3.863s | Local Loss: 0.4765 | Avg Loss: 0.4765 | LR: 0.000010
+[2026-01-09 19:57:23] Epoch: 185 | Iter: 000741 | Step Time: 3.749s | Local Loss: 0.0135 | Avg Loss: 0.0135 | LR: 0.000010
+[2026-01-09 19:57:27] Epoch: 185 | Iter: 000742 | Step Time: 3.405s | Local Loss: 0.2353 | Avg Loss: 0.2353 | LR: 0.000010
+[2026-01-09 19:57:30] Epoch: 185 | Iter: 000743 | Step Time: 3.445s | Local Loss: 0.1896 | Avg Loss: 0.1896 | LR: 0.000010
+[2026-01-09 19:57:39] Epoch: 186 | Iter: 000744 | Step Time: 3.893s | Local Loss: 0.1037 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-09 19:57:42] Epoch: 186 | Iter: 000745 | Step Time: 3.460s | Local Loss: 0.1538 | Avg Loss: 0.1538 | LR: 0.000010
+[2026-01-09 19:57:46] Epoch: 186 | Iter: 000746 | Step Time: 3.431s | Local Loss: 0.3613 | Avg Loss: 0.3613 | LR: 0.000010
+[2026-01-09 19:57:50] Epoch: 186 | Iter: 000747 | Step Time: 3.466s | Local Loss: 0.3905 | Avg Loss: 0.3905 | LR: 0.000010
+[2026-01-09 19:57:58] Epoch: 187 | Iter: 000748 | Step Time: 3.542s | Local Loss: 0.0667 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-09 19:58:01] Epoch: 187 | Iter: 000749 | Step Time: 3.420s | Local Loss: 0.0974 | Avg Loss: 0.0974 | LR: 0.000010
+[2026-01-09 19:59:01] Epoch: 187 | Iter: 000750 | Step Time: 3.473s | Local Loss: 0.0605 | Avg Loss: 0.0605 | LR: 0.000010
+[2026-01-09 19:59:05] Epoch: 187 | Iter: 000751 | Step Time: 3.460s | Local Loss: 0.3962 | Avg Loss: 0.3962 | LR: 0.000010
+[2026-01-09 19:59:14] Epoch: 188 | Iter: 000752 | Step Time: 4.060s | Local Loss: 0.0077 | Avg Loss: 0.0077 | LR: 0.000010
+[2026-01-09 19:59:17] Epoch: 188 | Iter: 000753 | Step Time: 3.475s | Local Loss: 0.0503 | Avg Loss: 0.0503 | LR: 0.000010
+[2026-01-09 19:59:21] Epoch: 188 | Iter: 000754 | Step Time: 3.443s | Local Loss: 0.0859 | Avg Loss: 0.0859 | LR: 0.000010
+[2026-01-09 19:59:24] Epoch: 188 | Iter: 000755 | Step Time: 3.450s | Local Loss: 0.0847 | Avg Loss: 0.0847 | LR: 0.000010
+[2026-01-09 19:59:33] Epoch: 189 | Iter: 000756 | Step Time: 3.958s | Local Loss: 0.1134 | Avg Loss: 0.1134 | LR: 0.000010
+[2026-01-09 19:59:37] Epoch: 189 | Iter: 000757 | Step Time: 3.489s | Local Loss: 0.0633 | Avg Loss: 0.0633 | LR: 0.000010
+[2026-01-09 19:59:40] Epoch: 189 | Iter: 000758 | Step Time: 3.450s | Local Loss: 0.0859 | Avg Loss: 0.0859 | LR: 0.000010
+[2026-01-09 19:59:44] Epoch: 189 | Iter: 000759 | Step Time: 3.449s | Local Loss: 0.0555 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-09 19:59:52] Epoch: 190 | Iter: 000760 | Step Time: 3.894s | Local Loss: 0.3063 | Avg Loss: 0.3063 | LR: 0.000010
+[2026-01-09 19:59:56] Epoch: 190 | Iter: 000761 | Step Time: 3.505s | Local Loss: 0.0179 | Avg Loss: 0.0179 | LR: 0.000010
+[2026-01-09 19:59:59] Epoch: 190 | Iter: 000762 | Step Time: 3.419s | Local Loss: 0.0174 | Avg Loss: 0.0174 | LR: 0.000010
+[2026-01-09 20:00:03] Epoch: 190 | Iter: 000763 | Step Time: 3.449s | Local Loss: 0.0083 | Avg Loss: 0.0083 | LR: 0.000010
+[2026-01-09 20:00:12] Epoch: 191 | Iter: 000764 | Step Time: 3.534s | Local Loss: 0.0410 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-09 20:00:15] Epoch: 191 | Iter: 000765 | Step Time: 3.428s | Local Loss: 0.0751 | Avg Loss: 0.0751 | LR: 0.000010
+[2026-01-09 20:00:19] Epoch: 191 | Iter: 000766 | Step Time: 3.531s | Local Loss: 0.3191 | Avg Loss: 0.3191 | LR: 0.000010
+[2026-01-09 20:00:22] Epoch: 191 | Iter: 000767 | Step Time: 3.478s | Local Loss: 0.4832 | Avg Loss: 0.4832 | LR: 0.000010
+[2026-01-09 20:00:30] Epoch: 192 | Iter: 000768 | Step Time: 3.887s | Local Loss: 0.2335 | Avg Loss: 0.2335 | LR: 0.000010
+[2026-01-09 20:00:34] Epoch: 192 | Iter: 000769 | Step Time: 3.459s | Local Loss: 0.0539 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-09 20:00:38] Epoch: 192 | Iter: 000770 | Step Time: 3.417s | Local Loss: 0.4876 | Avg Loss: 0.4876 | LR: 0.000010
+[2026-01-09 20:00:41] Epoch: 192 | Iter: 000771 | Step Time: 3.390s | Local Loss: 0.0000 | Avg Loss: 0.0000 | LR: 0.000010
+[2026-01-09 20:00:50] Epoch: 193 | Iter: 000772 | Step Time: 3.540s | Local Loss: 0.1666 | Avg Loss: 0.1666 | LR: 0.000010
+[2026-01-09 20:00:54] Epoch: 193 | Iter: 000773 | Step Time: 3.805s | Local Loss: 0.3628 | Avg Loss: 0.3628 | LR: 0.000010
+[2026-01-09 20:00:57] Epoch: 193 | Iter: 000774 | Step Time: 3.467s | Local Loss: 0.0993 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-09 20:01:01] Epoch: 193 | Iter: 000775 | Step Time: 3.439s | Local Loss: 0.1575 | Avg Loss: 0.1575 | LR: 0.000010
+[2026-01-09 20:01:09] Epoch: 194 | Iter: 000776 | Step Time: 3.545s | Local Loss: 0.1340 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-09 20:01:13] Epoch: 194 | Iter: 000777 | Step Time: 3.468s | Local Loss: 0.3265 | Avg Loss: 0.3265 | LR: 0.000010
+[2026-01-09 20:01:16] Epoch: 194 | Iter: 000778 | Step Time: 3.442s | Local Loss: 0.0383 | Avg Loss: 0.0383 | LR: 0.000010
+[2026-01-09 20:01:20] Epoch: 194 | Iter: 000779 | Step Time: 3.453s | Local Loss: 0.4166 | Avg Loss: 0.4166 | LR: 0.000010
+[2026-01-09 20:01:28] Epoch: 195 | Iter: 000780 | Step Time: 3.677s | Local Loss: 0.3001 | Avg Loss: 0.3001 | LR: 0.000010
+[2026-01-09 20:01:32] Epoch: 195 | Iter: 000781 | Step Time: 3.492s | Local Loss: 0.1385 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-09 20:01:35] Epoch: 195 | Iter: 000782 | Step Time: 3.445s | Local Loss: 0.5239 | Avg Loss: 0.5239 | LR: 0.000010
+[2026-01-09 20:01:39] Epoch: 195 | Iter: 000783 | Step Time: 3.485s | Local Loss: 0.5036 | Avg Loss: 0.5036 | LR: 0.000010
+[2026-01-09 20:01:47] Epoch: 196 | Iter: 000784 | Step Time: 3.547s | Local Loss: 0.2637 | Avg Loss: 0.2637 | LR: 0.000010
+[2026-01-09 20:01:51] Epoch: 196 | Iter: 000785 | Step Time: 3.762s | Local Loss: 0.0075 | Avg Loss: 0.0075 | LR: 0.000010
+[2026-01-09 20:01:54] Epoch: 196 | Iter: 000786 | Step Time: 3.450s | Local Loss: 0.2409 | Avg Loss: 0.2409 | LR: 0.000010
+[2026-01-09 20:01:58] Epoch: 196 | Iter: 000787 | Step Time: 3.438s | Local Loss: 0.2570 | Avg Loss: 0.2570 | LR: 0.000010
+[2026-01-09 20:02:06] Epoch: 197 | Iter: 000788 | Step Time: 3.522s | Local Loss: 0.1015 | Avg Loss: 0.1015 | LR: 0.000010
+[2026-01-09 20:02:10] Epoch: 197 | Iter: 000789 | Step Time: 3.399s | Local Loss: 0.0715 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-09 20:02:13] Epoch: 197 | Iter: 000790 | Step Time: 3.467s | Local Loss: 0.4408 | Avg Loss: 0.4408 | LR: 0.000010
+[2026-01-09 20:02:17] Epoch: 197 | Iter: 000791 | Step Time: 3.575s | Local Loss: 0.1195 | Avg Loss: 0.1195 | LR: 0.000010
+[2026-01-09 20:02:26] Epoch: 198 | Iter: 000792 | Step Time: 3.566s | Local Loss: 0.2458 | Avg Loss: 0.2458 | LR: 0.000010
+[2026-01-09 20:02:29] Epoch: 198 | Iter: 000793 | Step Time: 3.428s | Local Loss: 0.2882 | Avg Loss: 0.2882 | LR: 0.000010
+[2026-01-09 20:02:33] Epoch: 198 | Iter: 000794 | Step Time: 3.514s | Local Loss: 0.2578 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-09 20:02:36] Epoch: 198 | Iter: 000795 | Step Time: 3.470s | Local Loss: 0.0715 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-09 20:02:45] Epoch: 199 | Iter: 000796 | Step Time: 3.808s | Local Loss: 0.1341 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-09 20:02:48] Epoch: 199 | Iter: 000797 | Step Time: 3.440s | Local Loss: 0.2542 | Avg Loss: 0.2542 | LR: 0.000010
+[2026-01-09 20:02:52] Epoch: 199 | Iter: 000798 | Step Time: 3.469s | Local Loss: 0.2701 | Avg Loss: 0.2701 | LR: 0.000010
+[2026-01-09 20:02:56] Epoch: 199 | Iter: 000799 | Step Time: 3.465s | Local Loss: 0.0718 | Avg Loss: 0.0718 | LR: 0.000010
+[2026-01-09 20:03:43] Logger closing.
+[2026-01-11 18:29:37] Logger initialized. Current global_step: 0
+[2026-01-11 18:46:29] Logger initialized. Current global_step: 0
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/log/rank_1.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local/log/rank_1.log
new file mode 100644
index 0000000000000000000000000000000000000000..8e00e01fcc95a6fea57f47bcf9ee1158abe7ae8c
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+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/random_states_7.pkl
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+version https://git-lfs.github.com/spec/v1
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+size 16449
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/scheduler.bin b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/scheduler.bin
new file mode 100644
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+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/scheduler.bin
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+version https://git-lfs.github.com/spec/v1
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+size 1465
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/trainer_state.json b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/trainer_state.json
new file mode 100644
index 0000000000000000000000000000000000000000..1d95f114ee5317f202ceb17a4659b836a9508ef2
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/trainer_state.json
@@ -0,0 +1 @@
+{"global_step": 400}
\ No newline at end of file
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/weights.safetensors b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/weights.safetensors
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/zero_to_fp32.py b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..5995d6e6f04e43b989587aa9022a3aef0c66d694
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-400-epoch-400/zero_to_fp32.py
@@ -0,0 +1,760 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example:
+#   python zero_to_fp32.py . output_dir/
+#   or
+#   python zero_to_fp32.py . output_dir/ --safe_serialization
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+import gc
+import json
+import numpy as np
+from tqdm import tqdm
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+    total_files = len(files)
+    state_dicts = []
+    for f in tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
+
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
+    for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
+                                               output_dir,
+                                               max_shard_size="5GB",
+                                               safe_serialization=False,
+                                               tag=None,
+                                               exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_dir``: directory to the pytorch fp32 state_dict output files
+        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
+        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    # Dependency pre-check
+    if safe_serialization:
+        try:
+            from safetensors.torch import save_file
+        except ImportError:
+            print('If you want to use `safe_serialization`, please `pip install safetensors`')
+            raise
+    if max_shard_size is not None:
+        try:
+            from huggingface_hub import split_torch_state_dict_into_shards
+        except ImportError:
+            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
+            raise
+
+    # Convert zero checkpoint to state_dict
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                                          tag,
+                                                          exclude_frozen_parameters,
+                                                          lazy_mode=True)
+
+    # Shard the model if it is too big.
+    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
+    if max_shard_size is not None:
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
+                                                              filename_pattern=filename_pattern,
+                                                              max_shard_size=max_shard_size)
+    else:
+        from collections import namedtuple
+        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
+        state_dict_split = StateDictSplit(is_sharded=False,
+                                          filename_to_tensors={weights_name: list(state_dict.keys())})
+
+    # Save the model by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # Save index if sharded
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument("output_dir",
+                        type=str,
+                        help="directory to the pytorch fp32 state_dict output files"
+                        "(e.g. path/checkpoint-12-output/)")
+    parser.add_argument(
+        "--max_shard_size",
+        type=str,
+        default="5GB",
+        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
+        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
+        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
+        "without CPU OOM issues.")
+    parser.add_argument(
+        "--safe_serialization",
+        default=False,
+        action='store_true',
+        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_dir,
+                                               max_shard_size=args.max_shard_size,
+                                               safe_serialization=args.safe_serialization,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-450-epoch-450/custom_checkpoint_0.pkl b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-450-epoch-450/custom_checkpoint_0.pkl
new file mode 100644
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new file mode 100644
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-450-epoch-450/trainer_state.json b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-450-epoch-450/trainer_state.json
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-450-epoch-450/zero_to_fp32.py b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-450-epoch-450/zero_to_fp32.py
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+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example:
+#   python zero_to_fp32.py . output_dir/
+#   or
+#   python zero_to_fp32.py . output_dir/ --safe_serialization
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+import gc
+import json
+import numpy as np
+from tqdm import tqdm
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+    total_files = len(files)
+    state_dicts = []
+    for f in tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
+
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
+    for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
+                                               output_dir,
+                                               max_shard_size="5GB",
+                                               safe_serialization=False,
+                                               tag=None,
+                                               exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_dir``: directory to the pytorch fp32 state_dict output files
+        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
+        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    # Dependency pre-check
+    if safe_serialization:
+        try:
+            from safetensors.torch import save_file
+        except ImportError:
+            print('If you want to use `safe_serialization`, please `pip install safetensors`')
+            raise
+    if max_shard_size is not None:
+        try:
+            from huggingface_hub import split_torch_state_dict_into_shards
+        except ImportError:
+            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
+            raise
+
+    # Convert zero checkpoint to state_dict
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                                          tag,
+                                                          exclude_frozen_parameters,
+                                                          lazy_mode=True)
+
+    # Shard the model if it is too big.
+    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
+    if max_shard_size is not None:
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
+                                                              filename_pattern=filename_pattern,
+                                                              max_shard_size=max_shard_size)
+    else:
+        from collections import namedtuple
+        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
+        state_dict_split = StateDictSplit(is_sharded=False,
+                                          filename_to_tensors={weights_name: list(state_dict.keys())})
+
+    # Save the model by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # Save index if sharded
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument("output_dir",
+                        type=str,
+                        help="directory to the pytorch fp32 state_dict output files"
+                        "(e.g. path/checkpoint-12-output/)")
+    parser.add_argument(
+        "--max_shard_size",
+        type=str,
+        default="5GB",
+        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
+        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
+        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
+        "without CPU OOM issues.")
+    parser.add_argument(
+        "--safe_serialization",
+        default=False,
+        action='store_true',
+        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_dir,
+                                               max_shard_size=args.max_shard_size,
+                                               safe_serialization=args.safe_serialization,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/custom_checkpoint_0.pkl b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/custom_checkpoint_0.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..271edac9d4b7584e79e9762243dd40642d5ed02f
--- /dev/null
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/weights.safetensors b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/weights.safetensors
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/zero_to_fp32.py b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/zero_to_fp32.py
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@@ -0,0 +1,760 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example:
+#   python zero_to_fp32.py . output_dir/
+#   or
+#   python zero_to_fp32.py . output_dir/ --safe_serialization
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+import gc
+import json
+import numpy as np
+from tqdm import tqdm
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+    total_files = len(files)
+    state_dicts = []
+    for f in tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
+
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
+    for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
+                                               output_dir,
+                                               max_shard_size="5GB",
+                                               safe_serialization=False,
+                                               tag=None,
+                                               exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_dir``: directory to the pytorch fp32 state_dict output files
+        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
+        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    # Dependency pre-check
+    if safe_serialization:
+        try:
+            from safetensors.torch import save_file
+        except ImportError:
+            print('If you want to use `safe_serialization`, please `pip install safetensors`')
+            raise
+    if max_shard_size is not None:
+        try:
+            from huggingface_hub import split_torch_state_dict_into_shards
+        except ImportError:
+            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
+            raise
+
+    # Convert zero checkpoint to state_dict
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                                          tag,
+                                                          exclude_frozen_parameters,
+                                                          lazy_mode=True)
+
+    # Shard the model if it is too big.
+    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
+    if max_shard_size is not None:
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
+                                                              filename_pattern=filename_pattern,
+                                                              max_shard_size=max_shard_size)
+    else:
+        from collections import namedtuple
+        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
+        state_dict_split = StateDictSplit(is_sharded=False,
+                                          filename_to_tensors={weights_name: list(state_dict.keys())})
+
+    # Save the model by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # Save index if sharded
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument("output_dir",
+                        type=str,
+                        help="directory to the pytorch fp32 state_dict output files"
+                        "(e.g. path/checkpoint-12-output/)")
+    parser.add_argument(
+        "--max_shard_size",
+        type=str,
+        default="5GB",
+        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
+        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
+        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
+        "without CPU OOM issues.")
+    parser.add_argument(
+        "--safe_serialization",
+        default=False,
+        action='store_true',
+        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_dir,
+                                               max_shard_size=args.max_shard_size,
+                                               safe_serialization=args.safe_serialization,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_0/debug.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_0/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..faef9a266246a9c8bc5fa06aac85ed5525ad479c
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_0/debug.log
@@ -0,0 +1,10 @@
+================================================================================
+[debug_infer] epoch=0 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=0 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_0/ref_0/id0_img0.png b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_0/ref_0/id0_img0.png
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_1/debug.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_1/debug.log
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@@ -0,0 +1,10 @@
+================================================================================
+[debug_infer] epoch=1 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=1 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_1/ref_0/id0_img0.png b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_1/ref_0/id0_img0.png
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diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_10/debug.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_10/debug.log
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@@ -0,0 +1,10 @@
+================================================================================
+[debug_infer] epoch=10 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=10 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+================================================================================
+[debug_infer] epoch=101 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=103 index=0
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+================================================================================
+[debug_infer] epoch=104 index=0
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+================================================================================
+[debug_infer] epoch=105 index=0
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+================================================================================
+[debug_infer] epoch=106 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=11 index=0
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+================================================================================
+[debug_infer] epoch=110 index=0
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+================================================================================
+[debug_infer] epoch=111 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=112 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=116 index=0
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+================================================================================
+[debug_infer] epoch=117 index=0
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+================================================================================
+[debug_infer] epoch=118 index=0
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+================================================================================
+[debug_infer] epoch=119 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=120 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=124 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=130 index=0
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+================================================================================
+[debug_infer] epoch=131 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=137 index=0
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+================================================================================
+[debug_infer] epoch=138 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=14 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=141 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=143 index=0
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+================================================================================
+[debug_infer] epoch=144 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=148 index=0
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+================================================================================
+[debug_infer] epoch=149 index=0
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+================================================================================
+[debug_infer] epoch=15 index=0
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+================================================================================
+[debug_infer] epoch=150 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=154 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=156 index=0
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+================================================================================
+[debug_infer] epoch=157 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=159 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=160 index=0
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+================================================================================
+[debug_infer] epoch=161 index=0
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+================================================================================
+[debug_infer] epoch=162 index=0
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+================================================================================
+[debug_infer] epoch=163 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=169 index=0
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+================================================================================
+[debug_infer] epoch=17 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=174 index=0
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+================================================================================
+[debug_infer] epoch=175 index=0
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+================================================================================
+[debug_infer] epoch=176 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=18 index=0
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+================================================================================
+[debug_infer] epoch=180 index=0
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+================================================================================
+[debug_infer] epoch=181 index=0
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+================================================================================
+[debug_infer] epoch=182 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=186 index=0
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+================================================================================
+[debug_infer] epoch=187 index=0
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+================================================================================
+[debug_infer] epoch=188 index=0
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+================================================================================
+[debug_infer] epoch=189 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=190 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=192 index=0
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+================================================================================
+[debug_infer] epoch=193 index=0
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+================================================================================
+[debug_infer] epoch=194 index=0
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+================================================================================
+[debug_infer] epoch=195 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=199 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=2 index=0
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+================================================================================
+[debug_infer] epoch=20 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+================================================================================
+[debug_infer] epoch=200 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=202 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=205 index=0
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+================================================================================
+[debug_infer] epoch=206 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=209 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=212 index=0
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+================================================================================
+[debug_infer] epoch=213 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=219 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=230 index=0
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+================================================================================
+[debug_infer] epoch=231 index=0
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+================================================================================
+[debug_infer] epoch=232 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=239 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=249 index=0
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+================================================================================
+[debug_infer] epoch=25 index=0
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+================================================================================
+[debug_infer] epoch=250 index=0
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+================================================================================
+[debug_infer] epoch=251 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=255 index=0
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+================================================================================
+[debug_infer] epoch=256 index=0
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+================================================================================
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=258 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=263 index=0
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+================================================================================
+[debug_infer] epoch=264 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=268 index=0
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+================================================================================
+[debug_infer] epoch=269 index=0
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+================================================================================
+[debug_infer] epoch=27 index=0
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+================================================================================
+[debug_infer] epoch=270 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=272 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=277 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=280 index=0
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+================================================================================
+[debug_infer] epoch=281 index=0
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+================================================================================
+[debug_infer] epoch=282 index=0
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+================================================================================
+[debug_infer] epoch=283 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=288 index=0
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+================================================================================
+[debug_infer] epoch=289 index=0
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+================================================================================
+[debug_infer] epoch=29 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=296 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=300 index=0
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+================================================================================
+[debug_infer] epoch=301 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=312 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=318 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=32 index=0
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+================================================================================
+[debug_infer] epoch=320 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=326 index=0
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+================================================================================
+[debug_infer] epoch=327 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=332 index=0
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+================================================================================
+[debug_infer] epoch=333 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=344 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=346 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=348 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=35 index=0
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+================================================================================
+[debug_infer] epoch=350 index=0
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+================================================================================
+[debug_infer] epoch=351 index=0
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+================================================================================
+[debug_infer] epoch=352 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=358 index=0
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+================================================================================
+[debug_infer] epoch=359 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=362 index=0
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+================================================================================
+[debug_infer] epoch=363 index=0
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+================================================================================
+[debug_infer] epoch=364 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=365 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=367 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=369 index=0
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+================================================================================
+[debug_infer] epoch=37 index=0
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+================================================================================
+[debug_infer] epoch=370 index=0
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+================================================================================
+[debug_infer] epoch=371 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=375 index=0
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+================================================================================
+[debug_infer] epoch=376 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=377 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=378 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=381 index=0
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+================================================================================
+[debug_infer] epoch=382 index=0
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+================================================================================
+[debug_infer] epoch=383 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=386 index=0
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+================================================================================
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+================================================================================
+[debug_infer] epoch=388 index=0
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+================================================================================
+[debug_infer] epoch=389 index=0
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+================================================================================
+[debug_infer] epoch=39 index=0
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+================================================================================
+[debug_infer] epoch=390 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=394 index=0
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+================================================================================
+[debug_infer] epoch=395 index=0
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+================================================================================
+[debug_infer] epoch=396 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=56 index=0
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+================================================================================
+[debug_infer] epoch=57 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=58 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=61 index=0
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+================================================================================
+[debug_infer] epoch=62 index=0
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+================================================================================
+[debug_infer] epoch=63 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+================================================================================
+[debug_infer] epoch=64 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=68 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
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+================================================================================
+[debug_infer] epoch=69 index=0
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+================================================================================
+[debug_infer] epoch=7 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
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+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=70 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=75 index=0
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+================================================================================
+[debug_infer] epoch=76 index=0
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+================================================================================
+[debug_infer] epoch=77 index=0
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+================================================================================
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=81 index=0
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+================================================================================
+[debug_infer] epoch=82 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
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+================================================================================
+[debug_infer] epoch=83 index=0
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+================================================================================
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+================================================================================
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+================================================================================
+[debug_infer] epoch=87 index=0
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+================================================================================
+[debug_infer] epoch=88 index=0
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+================================================================================
+[debug_infer] epoch=89 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
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+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=9 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=93 index=0
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+================================================================================
+[debug_infer] epoch=94 index=0
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+================================================================================
+[debug_infer] epoch=95 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
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+================================================================================
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+================================================================================
+[debug_infer] epoch=96 index=0
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+num_frames=81 ref_num=3 ID_num=1
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+================================================================================
+[debug_infer] epoch=97 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=97 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
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+================================================================================
+[debug_infer] epoch=98 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=98 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_98/ref_0/id0_img0.png b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_98/ref_0/id0_img0.png
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+================================================================================
+[debug_infer] epoch=99 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+================================================================================
+[debug_infer] epoch=99 index=0
+video_path=/data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+num_frames=81 ref_num=3 ID_num=1
+prompt=['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。', '画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_99/ref_0/id0_img0.png b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/epoch_99/ref_0/id0_img0.png
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+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer
+Checkpoint: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/weights.safetensors
+Dataset split: all
+Dataset size: 5
+================================================================================
+[Sample 0]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data1/1.mp4
+text: 画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄
+shot_caption: ['画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/ref_images_0
+[GPU] before_infer allocated=17.94GB reserved=21.24GB
+[GPU] after_infer allocated=0.04GB reserved=0.09GB
+inference_time: 93.20s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/output_0.mp4
+Done.
+Train YAML: /data/rczhang/PencilFolder/multi-shot/multi_view/conf/multi-view.yaml
+Dataset JSON: /data/rczhang/PencilFolder/multi-shot/processed/dataset.json
+Output root: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1
+Output dir: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer
+Checkpoint: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/checkpoint-step-500-epoch-500/weights.safetensors
+Dataset split: all
+Dataset size: 5
+================================================================================
+[Sample 0]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data1/1.mp4
+text: 画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄
+shot_caption: ['画面右侧一位黄种人中年男性面向画面左侧，头部微微低垂，眼睛注视下方，表情严肃。他穿着深色西装和白色衬衫，头发整齐地梳向后方。背景可以看到一些家具轮廓，如椅子和柜子，整个场景给人一种安静、专注的感觉。该视频以下摇的运镜手法，在特写男子上半身的画面下拍摄']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/ref_images_0
+[GPU] before_infer allocated=17.94GB reserved=21.24GB
+[GPU] after_infer allocated=0.04GB reserved=0.11GB
+inference_time: 92.98s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/output_0.mp4
+================================================================================
+[Sample 1]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data1/2.mp4
+text: 画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。 画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄
+shot_caption: ['画面右侧的黄种人中年男性，穿着深色西装外套和白色衬衫，面朝镜头坐在沙发上，身体前倾，目光看向画面左侧的人，随后微微低头，又抬头看向画面左侧的人。画面左侧的人背对镜头，只露出部分头部和肩膀，穿着蓝色衣服，背景中可以看到木质墙面装饰，墙上挂有带有几何图案的装饰画，增添了空间的艺术感。透过窗户，自然光线洒入室内，营造出明亮而温馨的氛围。视频以晃动的运镜手法，在特写上半身的镜头下展示。 画面中间的黄种人中年男性面向画面左侧，目光专注地注视着前方。他的头部微微晃动。他穿着深色西装和白色衬衫，头发整齐地向后梳。背景是一面浅色墙壁和一扇带有百叶窗的窗户。整个场景光线柔和，营造出一种安静而专业的氛围。该视频以左摇的运镜手法，在特写胸部以上的画面下拍摄']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/ref_images_1
+[GPU] before_infer allocated=0.04GB reserved=0.11GB
+[GPU] after_infer allocated=0.04GB reserved=0.82GB
+inference_time: 83.00s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/output_1.mp4
+================================================================================
+[Sample 2]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data2/1.mp4
+text: 画面右侧的白人中年男性面向镜头，头部微微向左转动，眼睛注视着画面左侧的人物。他的表情严肃，嘴唇紧闭，显示出专注和思考的状态。他身穿深色衣物，肩部有复杂的纹理装饰。背景是一个昏暗的室内环境，光线柔和而集中，营造出一种紧张的氛围。该视频以静止运镜手法在特写上半身的镜头下展现。 画面右侧一位白人中年男性，他有着长长的银白色头发，身穿深色的盔甲。他的表情平静，目光专注地望向镜头，双手放在桌面上，手指轻轻触碰。画面左侧有一个人背对着镜头，没有拍摄到这个人的头部。背景是在一个昏暗的室内环境中，几支点燃的蜡烛散发着柔和的光芒，营造出一种温暖而神秘的氛围。该视频以晃动的手法，在特写人物上半身的画面下展示。
+shot_caption: ['画面右侧的白人中年男性面向镜头，头部微微向左转动，眼睛注视着画面左侧的人物。他的表情严肃，嘴唇紧闭，显示出专注和思考的状态。他身穿深色衣物，肩部有复杂的纹理装饰。背景是一个昏暗的室内环境，光线柔和而集中，营造出一种紧张的氛围。该视频以静止运镜手法在特写上半身的镜头下展现。 画面右侧一位白人中年男性，他有着长长的银白色头发，身穿深色的盔甲。他的表情平静，目光专注地望向镜头，双手放在桌面上，手指轻轻触碰。画面左侧有一个人背对着镜头，没有拍摄到这个人的头部。背景是在一个昏暗的室内环境中，几支点燃的蜡烛散发着柔和的光芒，营造出一种温暖而神秘的氛围。该视频以晃动的手法，在特写人物上半身的画面下展示。']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/ref_images_2
+[GPU] before_infer allocated=0.04GB reserved=0.82GB
+[GPU] after_infer allocated=0.04GB reserved=1.30GB
+inference_time: 82.37s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/output_2.mp4
+================================================================================
+[Sample 3]
+video_path: /data/rczhang/PencilFolder/multi-shot/processed/Data2/2.mp4
+text: 画面中间偏左的白人青年女性面向镜头，嘴巴微微张开，眼睛眨动着，身体和头部轻微晃动。她穿着黑色衣服，戴着金色项链和耳环。背景中可以看到一扇木质门和一些家具轮廓。整个场景营造出一种静谧而紧张的氛围。该视频以推同时晃动的运镜手法，在特写上半身的画面下展示。
+shot_caption: ['画面中间偏左的白人青年女性面向镜头，嘴巴微微张开，眼睛眨动着，身体和头部轻微晃动。她穿着黑色衣服，戴着金色项链和耳环。背景中可以看到一扇木质门和一些家具轮廓。整个场景营造出一种静谧而紧张的氛围。该视频以推同时晃动的运镜手法，在特写上半身的画面下展示。']
+num_frames: 81
+ref_num: 3, ID_num: 1
+saved ref images: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/ref_images_3
+[GPU] before_infer allocated=0.04GB reserved=1.30GB
+[GPU] after_infer allocated=0.04GB reserved=0.80GB
+inference_time: 82.88s
+saved output video: /data/rczhang/PencilFolder/multi-shot/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/debug_infer/output_3.mp4
+Done.
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+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 22.234s | Local Loss: 0.0240 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 15.162s | Local Loss: 0.5935 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 14.461s | Local Loss: 0.1183 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 13.738s | Local Loss: 0.4035 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 13.034s | Local Loss: 0.0923 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 15.430s | Local Loss: 0.3182 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 24.027s | Local Loss: 0.0298 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 14.745s | Local Loss: 0.0603 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 15.085s | Local Loss: 0.0858 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 13.934s | Local Loss: 0.4905 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 14.561s | Local Loss: 0.2712 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 14.891s | Local Loss: 0.1428 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 15.637s | Local Loss: 0.0935 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 15.802s | Local Loss: 0.1574 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 14.011s | Local Loss: 0.0815 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 13.491s | Local Loss: 0.2646 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 14.532s | Local Loss: 0.0979 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 15.110s | Local Loss: 0.1745 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 14.210s | Local Loss: 0.3879 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 14.664s | Local Loss: 0.2373 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 13.204s | Local Loss: 0.1606 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 14.230s | Local Loss: 0.2421 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 14.206s | Local Loss: 0.0487 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 12.712s | Local Loss: 0.1461 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 13.288s | Local Loss: 0.1145 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 12.999s | Local Loss: 0.1187 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 12.750s | Local Loss: 0.2254 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 14.390s | Local Loss: 0.1353 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 12.887s | Local Loss: 0.1386 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 14.142s | Local Loss: 0.1782 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 13.769s | Local Loss: 0.0600 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 12.993s | Local Loss: 0.0913 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 13.502s | Local Loss: 0.0692 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 14.642s | Local Loss: 0.0681 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 13.428s | Local Loss: 0.0877 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 13.358s | Local Loss: 0.1354 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 14.460s | Local Loss: 0.0945 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 13.028s | Local Loss: 0.0406 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 12.818s | Local Loss: 0.0369 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 13.751s | Local Loss: 0.1280 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 14.140s | Local Loss: 0.1469 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 13.874s | Local Loss: 0.2193 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 14.235s | Local Loss: 0.5645 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 13.728s | Local Loss: 0.0070 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 13.246s | Local Loss: 0.1364 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 13.406s | Local Loss: 0.1394 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 13.488s | Local Loss: 0.0897 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 13.248s | Local Loss: 0.0913 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 13.773s | Local Loss: 0.0411 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 13.528s | Local Loss: 0.0276 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 16.624s | Local Loss: 0.0642 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 13.184s | Local Loss: 0.1543 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 12.512s | Local Loss: 0.3653 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 12.921s | Local Loss: 0.3896 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 12.424s | Local Loss: 0.0348 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 12.029s | Local Loss: 0.0712 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 12.163s | Local Loss: 0.2156 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 12.381s | Local Loss: 0.0306 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 11.442s | Local Loss: 0.1856 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 11.704s | Local Loss: 0.1926 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 12.531s | Local Loss: 0.4196 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 13.381s | Local Loss: 0.1220 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 12.997s | Local Loss: 0.3763 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 12.300s | Local Loss: 0.4907 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 13.729s | Local Loss: 0.0654 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 13.032s | Local Loss: 0.0949 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 13.856s | Local Loss: 0.4288 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 11.972s | Local Loss: 0.0365 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 12.852s | Local Loss: 0.0701 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 12.579s | Local Loss: 0.4849 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 12.257s | Local Loss: 0.1041 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 11.870s | Local Loss: 0.0410 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 11.938s | Local Loss: 0.0144 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 12.569s | Local Loss: 0.1170 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 12.070s | Local Loss: 0.1691 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 13.137s | Local Loss: 0.0777 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 12.080s | Local Loss: 0.0761 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 12.111s | Local Loss: 0.1389 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 12.304s | Local Loss: 0.0441 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 11.645s | Local Loss: 0.2138 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 11.940s | Local Loss: 0.1042 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 12.105s | Local Loss: 0.0282 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 11.585s | Local Loss: 0.0064 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 12.454s | Local Loss: 0.1794 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 12.021s | Local Loss: 0.0339 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 11.446s | Local Loss: 0.1572 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 12.298s | Local Loss: 0.2087 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 11.926s | Local Loss: 0.0317 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 12.099s | Local Loss: 0.0693 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 11.850s | Local Loss: 0.0387 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 12.102s | Local Loss: 0.0878 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 11.958s | Local Loss: 0.0457 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 11.780s | Local Loss: 0.1446 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 12.385s | Local Loss: 0.0192 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 11.805s | Local Loss: 0.0952 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 11.865s | Local Loss: 0.3057 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 11.840s | Local Loss: 0.4525 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 12.540s | Local Loss: 0.1242 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 11.727s | Local Loss: 0.0845 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 12.052s | Local Loss: 0.0464 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 17.647s | Local Loss: 0.1756 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 14.136s | Local Loss: 0.0290 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 11.940s | Local Loss: 0.0954 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 12.105s | Local Loss: 0.0681 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 12.186s | Local Loss: 0.0967 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 13.231s | Local Loss: 0.3021 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 13.186s | Local Loss: 0.2601 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 11.834s | Local Loss: 0.5376 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 11.951s | Local Loss: 0.1094 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 13.271s | Local Loss: 0.3281 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 12.166s | Local Loss: 0.2875 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 13.308s | Local Loss: 0.4142 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 12.090s | Local Loss: 0.2019 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 11.706s | Local Loss: 0.1997 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 12.066s | Local Loss: 0.4286 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 11.900s | Local Loss: 0.0537 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 12.324s | Local Loss: 0.3179 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 12.117s | Local Loss: 0.0206 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 12.011s | Local Loss: 0.4074 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 12.040s | Local Loss: 0.0328 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 11.635s | Local Loss: 0.2395 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 12.057s | Local Loss: 0.0388 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 11.965s | Local Loss: 0.3048 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 12.095s | Local Loss: 0.5053 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 12.879s | Local Loss: 0.5257 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 11.553s | Local Loss: 0.0331 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 11.949s | Local Loss: 0.0383 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 11.990s | Local Loss: 0.0561 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 11.546s | Local Loss: 0.2716 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 12.944s | Local Loss: 0.4015 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 11.792s | Local Loss: 0.0248 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 12.238s | Local Loss: 0.0210 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 11.882s | Local Loss: 0.3548 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 11.675s | Local Loss: 0.1316 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 12.515s | Local Loss: 0.2275 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 12.650s | Local Loss: 0.0153 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 12.027s | Local Loss: 0.4018 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 12.956s | Local Loss: 0.0446 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 12.229s | Local Loss: 0.0527 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 12.834s | Local Loss: 0.0589 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 12.751s | Local Loss: 0.0379 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 12.758s | Local Loss: 0.0212 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 11.899s | Local Loss: 0.0168 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 12.176s | Local Loss: 0.1630 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 12.119s | Local Loss: 0.0310 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 12.276s | Local Loss: 0.0475 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 11.810s | Local Loss: 0.2219 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 12.283s | Local Loss: 0.3331 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 12.438s | Local Loss: 0.0593 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 11.935s | Local Loss: 0.0921 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 17.226s | Local Loss: 0.3927 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 11.916s | Local Loss: 0.0906 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 12.982s | Local Loss: 0.0913 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 13.277s | Local Loss: 0.1744 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 13.529s | Local Loss: 0.0369 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 12.210s | Local Loss: 0.0421 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 12.412s | Local Loss: 0.0161 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 13.437s | Local Loss: 0.0350 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 13.071s | Local Loss: 0.0635 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 12.365s | Local Loss: 0.0417 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 11.977s | Local Loss: 0.0803 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 11.872s | Local Loss: 0.1519 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 12.790s | Local Loss: 0.2121 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 12.732s | Local Loss: 0.0433 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 12.332s | Local Loss: 0.0234 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 12.008s | Local Loss: 0.1068 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 12.327s | Local Loss: 0.0245 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 13.570s | Local Loss: 0.1580 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 12.610s | Local Loss: 0.1651 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 11.855s | Local Loss: 0.2602 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 12.950s | Local Loss: 0.0263 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 12.344s | Local Loss: 0.2850 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 11.593s | Local Loss: 0.4928 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 12.439s | Local Loss: 0.0270 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 12.582s | Local Loss: 0.4703 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 11.746s | Local Loss: 0.2696 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 11.880s | Local Loss: 0.1443 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 11.895s | Local Loss: 0.0530 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 13.484s | Local Loss: 0.0433 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 13.839s | Local Loss: 0.0338 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 12.382s | Local Loss: 0.0298 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 12.764s | Local Loss: 0.0308 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 12.518s | Local Loss: 0.0174 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 12.256s | Local Loss: 0.0210 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 12.249s | Local Loss: 0.1315 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 12.321s | Local Loss: 0.0155 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 12.194s | Local Loss: 0.0206 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 12.336s | Local Loss: 0.2286 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 12.357s | Local Loss: 0.0322 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 12.026s | Local Loss: 0.3748 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 11.998s | Local Loss: 0.3686 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 11.941s | Local Loss: 0.4362 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 12.206s | Local Loss: 0.0894 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 14.907s | Local Loss: 0.2078 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 15.455s | Local Loss: 0.0126 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 14.211s | Local Loss: 0.0445 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 13.884s | Local Loss: 0.0371 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 14.382s | Local Loss: 0.0133 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 12.256s | Local Loss: 0.0488 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 12.756s | Local Loss: 0.3998 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 17.588s | Local Loss: 0.0396 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 12.814s | Local Loss: 0.2386 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 11.958s | Local Loss: 0.0945 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 13.692s | Local Loss: 0.0866 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 12.345s | Local Loss: 0.1084 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 12.220s | Local Loss: 0.0410 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 11.796s | Local Loss: 0.0873 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 13.147s | Local Loss: 0.0166 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 12.039s | Local Loss: 0.0315 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 11.636s | Local Loss: 0.0430 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 11.975s | Local Loss: 0.0471 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 20.903s | Local Loss: 0.0385 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 19.074s | Local Loss: 0.0487 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 12.493s | Local Loss: 0.0332 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 12.394s | Local Loss: 0.4447 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 15.034s | Local Loss: 0.0409 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 12.663s | Local Loss: 0.0662 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 13.582s | Local Loss: 0.0000 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 12.055s | Local Loss: 0.0313 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 13.838s | Local Loss: 0.0289 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 11.978s | Local Loss: 0.0728 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 12.305s | Local Loss: 0.0047 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 12.345s | Local Loss: 0.2814 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 12.401s | Local Loss: 0.0640 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 13.173s | Local Loss: 0.0159 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 12.320s | Local Loss: 0.0235 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 12.110s | Local Loss: 0.1749 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 12.180s | Local Loss: 0.2484 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 12.680s | Local Loss: 0.1134 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 13.041s | Local Loss: 0.3043 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 12.378s | Local Loss: 0.0233 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 12.907s | Local Loss: 0.0515 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 11.889s | Local Loss: 0.1637 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 12.487s | Local Loss: 0.0364 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 12.071s | Local Loss: 0.0042 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 12.135s | Local Loss: 0.0380 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 12.936s | Local Loss: 0.0246 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 11.853s | Local Loss: 0.0472 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 12.862s | Local Loss: 0.0844 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 12.381s | Local Loss: 0.0995 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 12.476s | Local Loss: 0.0269 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 13.021s | Local Loss: 0.1049 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 11.912s | Local Loss: 0.0194 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 12.382s | Local Loss: 0.1363 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 12.907s | Local Loss: 0.0516 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 12.384s | Local Loss: 0.0305 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 13.280s | Local Loss: 0.0147 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 11.843s | Local Loss: 0.0204 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 13.188s | Local Loss: 0.2047 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 12.494s | Local Loss: 0.1572 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 18.830s | Local Loss: 0.0279 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 12.867s | Local Loss: 0.0254 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 12.224s | Local Loss: 0.0127 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 14.128s | Local Loss: 0.0196 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 11.813s | Local Loss: 0.0726 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 12.592s | Local Loss: 0.0523 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 11.971s | Local Loss: 0.0143 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 13.214s | Local Loss: 0.1957 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 13.320s | Local Loss: 0.1776 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 11.697s | Local Loss: 0.0205 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 13.323s | Local Loss: 0.0288 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 11.822s | Local Loss: 0.0599 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 11.849s | Local Loss: 0.0242 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 13.131s | Local Loss: 0.0829 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 11.677s | Local Loss: 0.0869 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 12.932s | Local Loss: 0.0442 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 12.071s | Local Loss: 0.0225 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 11.675s | Local Loss: 0.0128 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 13.205s | Local Loss: 0.0731 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 11.677s | Local Loss: 0.1294 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 11.731s | Local Loss: 0.0157 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 12.489s | Local Loss: 0.0165 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 12.072s | Local Loss: 0.0658 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 12.585s | Local Loss: 0.0754 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 11.961s | Local Loss: 0.3565 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 11.839s | Local Loss: 0.0876 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 13.058s | Local Loss: 0.0743 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 11.886s | Local Loss: 0.0244 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 13.217s | Local Loss: 0.3912 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 12.566s | Local Loss: 0.4131 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 12.021s | Local Loss: 0.0207 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 13.497s | Local Loss: 0.0123 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 11.887s | Local Loss: 0.0587 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 12.278s | Local Loss: 0.0344 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 14.031s | Local Loss: 0.0242 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 11.594s | Local Loss: 0.0340 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 12.309s | Local Loss: 0.0127 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 12.408s | Local Loss: 0.0575 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 12.412s | Local Loss: 0.3693 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 12.258s | Local Loss: 0.0329 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 11.781s | Local Loss: 0.0122 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 12.549s | Local Loss: 0.1700 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 12.889s | Local Loss: 0.0169 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 12.325s | Local Loss: 0.0595 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 12.288s | Local Loss: 0.1972 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 11.894s | Local Loss: 0.0233 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 12.084s | Local Loss: 0.0248 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 12.769s | Local Loss: 0.2830 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 12.467s | Local Loss: 0.1885 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 12.547s | Local Loss: 0.0220 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 19.575s | Local Loss: 0.0779 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 14.187s | Local Loss: 0.0889 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 12.398s | Local Loss: 0.0443 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 14.279s | Local Loss: 0.0800 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 13.941s | Local Loss: 0.0784 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 15.752s | Local Loss: 0.1268 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 12.628s | Local Loss: 0.0000 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 14.230s | Local Loss: 0.0099 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 12.851s | Local Loss: 0.0632 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 13.628s | Local Loss: 0.0919 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 12.830s | Local Loss: 0.0868 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 12.627s | Local Loss: 0.0123 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 12.887s | Local Loss: 0.0075 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 12.426s | Local Loss: 0.0316 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 13.791s | Local Loss: 0.0437 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 11.704s | Local Loss: 0.1827 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 12.064s | Local Loss: 0.0802 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 12.634s | Local Loss: 0.0038 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 12.842s | Local Loss: 0.0243 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 12.374s | Local Loss: 0.0036 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 11.704s | Local Loss: 0.0000 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 12.022s | Local Loss: 0.0298 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 12.593s | Local Loss: 0.0160 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 15.076s | Local Loss: 0.0305 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 14.088s | Local Loss: 0.0102 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 13.664s | Local Loss: 0.0229 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 15.263s | Local Loss: 0.0104 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 13.165s | Local Loss: 0.0722 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 12.460s | Local Loss: 0.0211 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 13.127s | Local Loss: 0.2985 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 13.424s | Local Loss: 0.0815 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 13.399s | Local Loss: 0.0111 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 13.052s | Local Loss: 0.0480 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 12.167s | Local Loss: 0.0111 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 13.550s | Local Loss: 0.1511 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 12.214s | Local Loss: 0.0099 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 12.541s | Local Loss: 0.0189 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 13.057s | Local Loss: 0.0272 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 12.910s | Local Loss: 0.0445 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 14.752s | Local Loss: 0.0213 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 14.063s | Local Loss: 0.0114 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 13.684s | Local Loss: 0.2110 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 13.088s | Local Loss: 0.0217 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 12.869s | Local Loss: 0.0266 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 12.592s | Local Loss: 0.0683 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 12.044s | Local Loss: 0.0401 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 12.501s | Local Loss: 0.0602 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 12.111s | Local Loss: 0.0094 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 12.825s | Local Loss: 0.0126 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 12.166s | Local Loss: 0.0643 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 16.259s | Local Loss: 0.0150 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 14.049s | Local Loss: 0.0707 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 14.779s | Local Loss: 0.0134 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 13.548s | Local Loss: 0.0169 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 14.238s | Local Loss: 0.0102 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 13.764s | Local Loss: 0.0164 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 12.327s | Local Loss: 0.0832 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 13.815s | Local Loss: 0.0286 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 11.784s | Local Loss: 0.0245 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 16.549s | Local Loss: 0.0183 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 21.487s | Local Loss: 0.0162 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 14.225s | Local Loss: 0.0259 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 12.386s | Local Loss: 0.0254 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 12.233s | Local Loss: 0.0693 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 12.429s | Local Loss: 0.0189 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 12.145s | Local Loss: 0.0128 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 12.648s | Local Loss: 0.0281 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 12.579s | Local Loss: 0.0279 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 12.622s | Local Loss: 0.0353 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 11.997s | Local Loss: 0.0154 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 11.650s | Local Loss: 0.0301 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 11.414s | Local Loss: 0.0306 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 12.100s | Local Loss: 0.0203 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 12.506s | Local Loss: 0.0087 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 13.034s | Local Loss: 0.1180 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 12.593s | Local Loss: 0.0221 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 12.052s | Local Loss: 0.0158 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 11.814s | Local Loss: 0.1762 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 11.525s | Local Loss: 0.0666 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 13.267s | Local Loss: 0.0229 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 11.320s | Local Loss: 0.0121 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 12.088s | Local Loss: 0.0257 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 11.697s | Local Loss: 0.0000 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 11.685s | Local Loss: 0.0253 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 12.824s | Local Loss: 0.0160 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 11.552s | Local Loss: 0.0102 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 11.360s | Local Loss: 0.1149 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 12.818s | Local Loss: 0.0103 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 11.707s | Local Loss: 0.2689 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 11.859s | Local Loss: 0.0557 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 11.941s | Local Loss: 0.0080 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 11.660s | Local Loss: 0.0117 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 12.487s | Local Loss: 0.0190 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 11.640s | Local Loss: 0.0134 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 11.682s | Local Loss: 0.0576 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 12.294s | Local Loss: 0.0287 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 11.541s | Local Loss: 0.0255 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 12.692s | Local Loss: 0.1398 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 11.415s | Local Loss: 0.0145 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 11.628s | Local Loss: 0.0211 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 17.062s | Local Loss: 0.0136 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 12.513s | Local Loss: 0.0117 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 12.135s | Local Loss: 0.0343 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 12.488s | Local Loss: 0.1476 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 12.235s | Local Loss: 0.0560 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 12.872s | Local Loss: 0.0141 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 13.694s | Local Loss: 0.0179 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 12.749s | Local Loss: 0.0795 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 11.418s | Local Loss: 0.0357 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 12.604s | Local Loss: 0.1558 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 11.919s | Local Loss: 0.0202 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 11.631s | Local Loss: 0.0397 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 11.634s | Local Loss: 0.0652 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 12.396s | Local Loss: 0.0132 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 12.248s | Local Loss: 0.0159 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 11.701s | Local Loss: 0.0342 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 12.542s | Local Loss: 0.0195 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 11.922s | Local Loss: 0.0157 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 11.435s | Local Loss: 0.0180 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 12.718s | Local Loss: 0.0132 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 12.378s | Local Loss: 0.1040 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 11.658s | Local Loss: 0.1175 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 11.716s | Local Loss: 0.0196 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 11.365s | Local Loss: 0.0475 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 11.835s | Local Loss: 0.1216 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 12.021s | Local Loss: 0.0108 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 11.378s | Local Loss: 0.0154 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 11.621s | Local Loss: 0.0467 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 11.947s | Local Loss: 0.0209 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 12.131s | Local Loss: 0.0252 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 12.026s | Local Loss: 0.0110 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 11.994s | Local Loss: 0.0757 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 12.353s | Local Loss: 0.0680 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 11.754s | Local Loss: 0.0117 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 12.999s | Local Loss: 0.0087 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 11.349s | Local Loss: 0.2421 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 11.879s | Local Loss: 0.0066 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 11.848s | Local Loss: 0.0072 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 11.729s | Local Loss: 0.0056 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 12.267s | Local Loss: 0.0240 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 11.394s | Local Loss: 0.0030 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 12.342s | Local Loss: 0.1101 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 12.247s | Local Loss: 0.0919 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 11.532s | Local Loss: 0.0174 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 12.188s | Local Loss: 0.0126 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 11.729s | Local Loss: 0.0138 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 12.411s | Local Loss: 0.0087 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 11.818s | Local Loss: 0.0244 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 12.041s | Local Loss: 0.2124 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 12.682s | Local Loss: 0.1130 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 16.680s | Local Loss: 0.0502 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 12.736s | Local Loss: 0.0074 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 11.626s | Local Loss: 0.0417 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 12.311s | Local Loss: 0.3158 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 12.534s | Local Loss: 0.0517 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 12.205s | Local Loss: 0.0174 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 13.898s | Local Loss: 0.0720 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 13.937s | Local Loss: 0.0284 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 13.540s | Local Loss: 0.0450 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 12.755s | Local Loss: 0.0270 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 13.057s | Local Loss: 0.2237 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 13.661s | Local Loss: 0.0157 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 12.706s | Local Loss: 0.0068 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 11.950s | Local Loss: 0.0166 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 12.204s | Local Loss: 0.0634 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 12.376s | Local Loss: 0.0263 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 11.646s | Local Loss: 0.0085 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 11.785s | Local Loss: 0.0127 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 13.768s | Local Loss: 0.0098 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 11.456s | Local Loss: 0.0423 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 12.214s | Local Loss: 0.0086 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 12.255s | Local Loss: 0.0982 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 11.623s | Local Loss: 0.0384 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 12.535s | Local Loss: 0.0114 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 11.341s | Local Loss: 0.0094 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 12.133s | Local Loss: 0.0367 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 12.004s | Local Loss: 0.0051 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 12.898s | Local Loss: 0.0239 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 11.599s | Local Loss: 0.1134 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 12.553s | Local Loss: 0.0079 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 13.096s | Local Loss: 0.0079 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 12.467s | Local Loss: 0.0220 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 12.575s | Local Loss: 0.0055 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 12.601s | Local Loss: 0.0035 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 12.722s | Local Loss: 0.0373 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 11.963s | Local Loss: 0.0480 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 12.901s | Local Loss: 0.0348 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 12.911s | Local Loss: 0.0087 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 11.550s | Local Loss: 0.0071 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 12.435s | Local Loss: 0.1060 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 12.383s | Local Loss: 0.0620 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 11.643s | Local Loss: 0.0079 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 11.513s | Local Loss: 0.0196 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 13.980s | Local Loss: 0.3323 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 11.343s | Local Loss: 0.0189 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 12.044s | Local Loss: 0.0093 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 12.064s | Local Loss: 0.0079 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 11.735s | Local Loss: 0.0755 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 12.547s | Local Loss: 0.0462 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 11.626s | Local Loss: 0.0080 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 16.897s | Local Loss: 0.0240 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 14.559s | Local Loss: 0.5936 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.772s | Local Loss: 0.1184 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.569s | Local Loss: 0.4035 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 13.455s | Local Loss: 0.0922 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 12.560s | Local Loss: 0.3185 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.840s | Local Loss: 0.0298 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 13.036s | Local Loss: 0.0605 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.634s | Local Loss: 0.0861 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 15.644s | Local Loss: 0.4918 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 14.507s | Local Loss: 0.2713 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.614s | Local Loss: 0.1430 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 14.064s | Local Loss: 0.0933 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.582s | Local Loss: 0.1576 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 13.301s | Local Loss: 0.0819 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.829s | Local Loss: 0.2647 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 12.386s | Local Loss: 0.0983 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 12.443s | Local Loss: 0.1746 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 12.370s | Local Loss: 0.3883 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 12.273s | Local Loss: 0.2376 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 13.329s | Local Loss: 0.1604 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 12.076s | Local Loss: 0.2423 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 13.507s | Local Loss: 0.0487 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 12.309s | Local Loss: 0.1465 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.580s | Local Loss: 0.1145 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.785s | Local Loss: 0.1191 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 12.274s | Local Loss: 0.2261 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 12.266s | Local Loss: 0.1358 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.725s | Local Loss: 0.1394 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.755s | Local Loss: 0.1788 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.928s | Local Loss: 0.0605 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 13.729s | Local Loss: 0.0921 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 12.033s | Local Loss: 0.0703 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 12.120s | Local Loss: 0.0679 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.763s | Local Loss: 0.0884 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.792s | Local Loss: 0.1361 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 12.195s | Local Loss: 0.0951 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.747s | Local Loss: 0.0400 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 12.233s | Local Loss: 0.0377 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 12.123s | Local Loss: 0.1289 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 12.541s | Local Loss: 0.1482 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.923s | Local Loss: 0.2193 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 12.533s | Local Loss: 0.5655 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 13.391s | Local Loss: 0.0069 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 12.135s | Local Loss: 0.1372 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 12.356s | Local Loss: 0.1400 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 12.396s | Local Loss: 0.0902 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 12.183s | Local Loss: 0.0937 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 12.197s | Local Loss: 0.0428 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 12.417s | Local Loss: 0.0307 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 16.882s | Local Loss: 0.0643 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 12.144s | Local Loss: 0.1545 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 12.546s | Local Loss: 0.3654 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 12.141s | Local Loss: 0.3892 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 11.932s | Local Loss: 0.0343 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 12.428s | Local Loss: 0.0706 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 13.485s | Local Loss: 0.2166 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 11.920s | Local Loss: 0.0303 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 11.729s | Local Loss: 0.1869 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 11.974s | Local Loss: 0.1941 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 12.049s | Local Loss: 0.4209 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 11.585s | Local Loss: 0.1226 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 12.106s | Local Loss: 0.3766 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 11.660s | Local Loss: 0.4913 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 11.669s | Local Loss: 0.0654 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 11.804s | Local Loss: 0.0948 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 11.598s | Local Loss: 0.4303 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 11.713s | Local Loss: 0.0393 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 11.532s | Local Loss: 0.0709 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 11.705s | Local Loss: 0.4858 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 11.615s | Local Loss: 0.1047 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 11.629s | Local Loss: 0.0416 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 11.896s | Local Loss: 0.0147 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 11.727s | Local Loss: 0.1179 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 11.497s | Local Loss: 0.1704 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 12.011s | Local Loss: 0.0784 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 11.520s | Local Loss: 0.0775 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 11.734s | Local Loss: 0.1408 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 12.002s | Local Loss: 0.0448 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 11.625s | Local Loss: 0.2159 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 11.540s | Local Loss: 0.1075 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 11.534s | Local Loss: 0.0282 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 11.420s | Local Loss: 0.0064 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 11.902s | Local Loss: 0.1819 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 11.435s | Local Loss: 0.0337 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 11.541s | Local Loss: 0.1584 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 11.940s | Local Loss: 0.2107 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 11.500s | Local Loss: 0.0324 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 11.643s | Local Loss: 0.0722 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 11.455s | Local Loss: 0.0362 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 11.343s | Local Loss: 0.0881 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 11.970s | Local Loss: 0.0424 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 11.380s | Local Loss: 0.1454 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 11.756s | Local Loss: 0.0189 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 11.762s | Local Loss: 0.1001 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 11.568s | Local Loss: 0.3074 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 11.861s | Local Loss: 0.4534 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 11.379s | Local Loss: 0.1253 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 11.443s | Local Loss: 0.0877 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 11.837s | Local Loss: 0.0469 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 14.664s | Local Loss: 0.1794 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 11.640s | Local Loss: 0.0309 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 12.987s | Local Loss: 0.0965 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 11.355s | Local Loss: 0.0697 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 11.954s | Local Loss: 0.0977 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 12.382s | Local Loss: 0.3022 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 12.147s | Local Loss: 0.2621 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 13.220s | Local Loss: 0.5381 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 11.532s | Local Loss: 0.1121 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 12.240s | Local Loss: 0.3298 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 11.506s | Local Loss: 0.2898 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 11.835s | Local Loss: 0.4144 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 11.534s | Local Loss: 0.2037 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 11.363s | Local Loss: 0.1997 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 11.717s | Local Loss: 0.4304 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 11.553s | Local Loss: 0.0504 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 11.459s | Local Loss: 0.3183 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 11.831s | Local Loss: 0.0225 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 11.939s | Local Loss: 0.4101 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 11.518s | Local Loss: 0.0340 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 11.760s | Local Loss: 0.2420 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 11.701s | Local Loss: 0.0394 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 11.343s | Local Loss: 0.3073 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 11.643s | Local Loss: 0.5072 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 11.496s | Local Loss: 0.5272 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 12.277s | Local Loss: 0.0331 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 11.673s | Local Loss: 0.0387 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 11.387s | Local Loss: 0.0523 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 11.980s | Local Loss: 0.2736 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 11.871s | Local Loss: 0.4022 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 11.418s | Local Loss: 0.0239 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 11.749s | Local Loss: 0.0213 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 11.658s | Local Loss: 0.3579 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 11.966s | Local Loss: 0.1334 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 11.751s | Local Loss: 0.2310 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 11.205s | Local Loss: 0.0168 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 11.986s | Local Loss: 0.4035 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 11.646s | Local Loss: 0.0463 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 11.684s | Local Loss: 0.0522 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 11.862s | Local Loss: 0.0555 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 12.040s | Local Loss: 0.0389 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 11.970s | Local Loss: 0.0215 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 11.902s | Local Loss: 0.0132 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 11.383s | Local Loss: 0.1643 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 12.075s | Local Loss: 0.0295 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 11.564s | Local Loss: 0.0487 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 11.606s | Local Loss: 0.2246 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 11.474s | Local Loss: 0.3360 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 11.601s | Local Loss: 0.0604 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 11.814s | Local Loss: 0.0923 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 14.801s | Local Loss: 0.3959 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 12.076s | Local Loss: 0.0907 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 12.953s | Local Loss: 0.0918 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 11.681s | Local Loss: 0.1766 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 12.699s | Local Loss: 0.0376 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 11.528s | Local Loss: 0.0404 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 12.223s | Local Loss: 0.0151 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 11.918s | Local Loss: 0.0344 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 12.706s | Local Loss: 0.0641 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 11.664s | Local Loss: 0.0422 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 14.071s | Local Loss: 0.0811 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 11.314s | Local Loss: 0.1533 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 12.141s | Local Loss: 0.2142 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 11.698s | Local Loss: 0.0437 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 11.690s | Local Loss: 0.0224 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 11.727s | Local Loss: 0.1087 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 11.281s | Local Loss: 0.0267 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 11.956s | Local Loss: 0.1597 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 11.507s | Local Loss: 0.1681 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 11.332s | Local Loss: 0.2623 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 11.747s | Local Loss: 0.0268 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 11.302s | Local Loss: 0.2842 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 11.701s | Local Loss: 0.4949 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 11.372s | Local Loss: 0.0279 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 11.497s | Local Loss: 0.4740 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 11.872s | Local Loss: 0.2697 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 11.632s | Local Loss: 0.1468 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 11.181s | Local Loss: 0.0535 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 11.899s | Local Loss: 0.0446 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 11.808s | Local Loss: 0.0323 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 11.700s | Local Loss: 0.0305 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 11.646s | Local Loss: 0.0307 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 11.644s | Local Loss: 0.0209 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 11.624s | Local Loss: 0.0207 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 11.755s | Local Loss: 0.1343 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 11.425s | Local Loss: 0.0164 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 11.784s | Local Loss: 0.0209 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 11.561s | Local Loss: 0.2314 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 11.382s | Local Loss: 0.0349 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 11.800s | Local Loss: 0.3747 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 11.570s | Local Loss: 0.3710 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 11.489s | Local Loss: 0.4370 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 11.926s | Local Loss: 0.0907 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 11.612s | Local Loss: 0.2090 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 11.723s | Local Loss: 0.0131 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 11.344s | Local Loss: 0.0445 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 11.439s | Local Loss: 0.0397 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 11.977s | Local Loss: 0.0103 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 11.823s | Local Loss: 0.0481 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 11.325s | Local Loss: 0.4026 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 14.767s | Local Loss: 0.0419 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 12.157s | Local Loss: 0.2372 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 11.914s | Local Loss: 0.0958 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 12.043s | Local Loss: 0.0875 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 12.054s | Local Loss: 0.1082 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 11.739s | Local Loss: 0.0412 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 12.264s | Local Loss: 0.0882 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 12.138s | Local Loss: 0.0139 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 11.625s | Local Loss: 0.0318 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 11.670s | Local Loss: 0.0430 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 12.188s | Local Loss: 0.0471 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 12.023s | Local Loss: 0.0389 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 11.498s | Local Loss: 0.0500 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 11.945s | Local Loss: 0.0345 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 12.062s | Local Loss: 0.4471 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 12.030s | Local Loss: 0.0419 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 11.518s | Local Loss: 0.0664 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 13.230s | Local Loss: 0.0000 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 11.462s | Local Loss: 0.0359 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 11.829s | Local Loss: 0.0284 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 11.761s | Local Loss: 0.0733 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 11.618s | Local Loss: 0.0049 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 11.468s | Local Loss: 0.2846 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 11.688s | Local Loss: 0.0658 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 11.329s | Local Loss: 0.0190 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 11.842s | Local Loss: 0.0230 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 11.758s | Local Loss: 0.1763 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 11.607s | Local Loss: 0.2512 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 11.454s | Local Loss: 0.1156 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 11.566s | Local Loss: 0.3073 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 11.416s | Local Loss: 0.0368 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 11.668s | Local Loss: 0.0521 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 11.732s | Local Loss: 0.1688 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 11.691s | Local Loss: 0.0380 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 11.569s | Local Loss: 0.0040 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 11.617s | Local Loss: 0.0402 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 11.650s | Local Loss: 0.0249 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 11.757s | Local Loss: 0.0500 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 11.786s | Local Loss: 0.0879 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 11.404s | Local Loss: 0.1014 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 11.557s | Local Loss: 0.0259 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 12.043s | Local Loss: 0.1071 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 11.626s | Local Loss: 0.0214 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 11.510s | Local Loss: 0.1421 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 12.017s | Local Loss: 0.0532 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 11.669s | Local Loss: 0.0320 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 11.729s | Local Loss: 0.0197 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 11.908s | Local Loss: 0.0236 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 11.620s | Local Loss: 0.2087 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 11.626s | Local Loss: 0.1610 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 14.961s | Local Loss: 0.0270 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 12.134s | Local Loss: 0.0259 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 12.563s | Local Loss: 0.0122 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 11.954s | Local Loss: 0.0210 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 12.214s | Local Loss: 0.0750 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 13.355s | Local Loss: 0.0541 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 12.330s | Local Loss: 0.0145 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 11.756s | Local Loss: 0.1995 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 11.685s | Local Loss: 0.1821 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 12.003s | Local Loss: 0.0189 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 11.424s | Local Loss: 0.0293 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 11.876s | Local Loss: 0.0614 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 11.725s | Local Loss: 0.0263 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 11.562s | Local Loss: 0.0830 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 11.799s | Local Loss: 0.0869 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 11.550s | Local Loss: 0.0437 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 11.391s | Local Loss: 0.0255 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 12.351s | Local Loss: 0.0121 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 11.368s | Local Loss: 0.0751 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 11.412s | Local Loss: 0.1311 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 11.607s | Local Loss: 0.0152 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 11.540s | Local Loss: 0.0124 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 19.439s | Local Loss: 0.0674 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 18.909s | Local Loss: 0.0771 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 20.844s | Local Loss: 0.3570 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 18.607s | Local Loss: 0.0891 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 18.681s | Local Loss: 0.0751 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 19.109s | Local Loss: 0.0243 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 23.219s | Local Loss: 0.3917 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 18.368s | Local Loss: 0.4135 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 19.130s | Local Loss: 0.0219 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 18.360s | Local Loss: 0.0100 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 19.734s | Local Loss: 0.0594 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 18.266s | Local Loss: 0.0344 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 19.063s | Local Loss: 0.0241 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 22.138s | Local Loss: 0.0339 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 19.762s | Local Loss: 0.0122 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 18.176s | Local Loss: 0.0578 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 20.521s | Local Loss: 0.3708 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 19.004s | Local Loss: 0.0332 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 23.844s | Local Loss: 0.0125 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 18.181s | Local Loss: 0.1708 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 19.545s | Local Loss: 0.0176 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 19.026s | Local Loss: 0.0588 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 19.593s | Local Loss: 0.2043 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 14.826s | Local Loss: 0.0261 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 11.329s | Local Loss: 0.0253 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 11.622s | Local Loss: 0.2870 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 11.737s | Local Loss: 0.1891 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 11.749s | Local Loss: 0.0243 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 14.876s | Local Loss: 0.0810 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 12.271s | Local Loss: 0.0858 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 12.287s | Local Loss: 0.0493 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 11.927s | Local Loss: 0.0848 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 11.456s | Local Loss: 0.0782 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 12.242s | Local Loss: 0.1320 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 11.929s | Local Loss: 0.0000 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 12.086s | Local Loss: 0.0117 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 11.871s | Local Loss: 0.0635 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 11.822s | Local Loss: 0.0911 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 12.775s | Local Loss: 0.0891 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 11.653s | Local Loss: 0.0132 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 11.684s | Local Loss: 0.0072 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 11.574s | Local Loss: 0.0330 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 11.261s | Local Loss: 0.0449 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 11.642s | Local Loss: 0.1827 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 12.126s | Local Loss: 0.0802 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 11.583s | Local Loss: 0.0037 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 11.502s | Local Loss: 0.0220 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 11.889s | Local Loss: 0.0037 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 11.643s | Local Loss: 0.0000 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 12.319s | Local Loss: 0.0335 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 12.678s | Local Loss: 0.0150 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 12.369s | Local Loss: 0.0312 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 12.354s | Local Loss: 0.0100 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 12.562s | Local Loss: 0.0265 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 12.115s | Local Loss: 0.0095 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 11.821s | Local Loss: 0.0677 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 12.011s | Local Loss: 0.0203 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 11.621s | Local Loss: 0.2979 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 12.146s | Local Loss: 0.0800 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 11.753s | Local Loss: 0.0124 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 11.906s | Local Loss: 0.0478 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 11.918s | Local Loss: 0.0101 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 11.824s | Local Loss: 0.1511 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 11.672s | Local Loss: 0.0092 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 12.192s | Local Loss: 0.0183 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 11.564s | Local Loss: 0.0262 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 11.463s | Local Loss: 0.0434 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 12.284s | Local Loss: 0.0237 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 11.347s | Local Loss: 0.0115 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 11.547s | Local Loss: 0.2057 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 11.559s | Local Loss: 0.0181 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 11.375s | Local Loss: 0.0222 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 12.054s | Local Loss: 0.0654 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 12.279s | Local Loss: 0.0395 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 11.650s | Local Loss: 0.0590 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 12.113s | Local Loss: 0.0091 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 11.627s | Local Loss: 0.0124 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 11.751s | Local Loss: 0.0654 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 14.928s | Local Loss: 0.0149 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 12.635s | Local Loss: 0.0687 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 11.918s | Local Loss: 0.0150 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 12.278s | Local Loss: 0.0094 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 12.047s | Local Loss: 0.0064 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 13.988s | Local Loss: 0.0196 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 12.275s | Local Loss: 0.0827 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 13.188s | Local Loss: 0.0297 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 12.350s | Local Loss: 0.0257 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 12.534s | Local Loss: 0.0196 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 13.085s | Local Loss: 0.0173 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 13.244s | Local Loss: 0.0270 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 12.034s | Local Loss: 0.0259 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 19.399s | Local Loss: 0.0709 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 14.857s | Local Loss: 0.0191 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 13.672s | Local Loss: 0.0138 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 12.229s | Local Loss: 0.0287 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 11.879s | Local Loss: 0.0286 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 11.563s | Local Loss: 0.0360 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 11.714s | Local Loss: 0.0167 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 11.692s | Local Loss: 0.0297 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 11.220s | Local Loss: 0.0321 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 11.737s | Local Loss: 0.0204 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 11.347s | Local Loss: 0.0090 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 11.532s | Local Loss: 0.1226 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 11.806s | Local Loss: 0.0225 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 11.418s | Local Loss: 0.0177 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 11.768s | Local Loss: 0.1760 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 11.452s | Local Loss: 0.0671 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 11.214s | Local Loss: 0.0233 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 11.636s | Local Loss: 0.0126 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 11.492s | Local Loss: 0.0261 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 11.776s | Local Loss: 0.0000 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 11.377s | Local Loss: 0.0272 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 11.543s | Local Loss: 0.0177 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 11.598s | Local Loss: 0.0103 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 11.173s | Local Loss: 0.1225 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 11.592s | Local Loss: 0.0117 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 11.457s | Local Loss: 0.2743 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 11.498s | Local Loss: 0.0570 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 11.476s | Local Loss: 0.0073 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 12.154s | Local Loss: 0.0115 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 14.589s | Local Loss: 0.0149 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 12.621s | Local Loss: 0.0153 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 12.626s | Local Loss: 0.0594 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 11.819s | Local Loss: 0.0310 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 12.732s | Local Loss: 0.0264 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_1.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_1.log
new file mode 100644
index 0000000000000000000000000000000000000000..0ffda369ece62183007cdb5f922d77d47e9dc3b9
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_1.log
@@ -0,0 +1,900 @@
+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 17.922s | Local Loss: 0.5407 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 14.574s | Local Loss: 0.3472 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 13.739s | Local Loss: 0.1272 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 13.101s | Local Loss: 0.1964 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 12.289s | Local Loss: 0.1666 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 14.669s | Local Loss: 0.2428 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 23.100s | Local Loss: 0.0570 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 13.877s | Local Loss: 0.3556 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 14.225s | Local Loss: 0.2422 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 13.205s | Local Loss: 0.0989 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 13.950s | Local Loss: 0.0087 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 14.185s | Local Loss: 0.3489 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 14.950s | Local Loss: 0.5304 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 15.040s | Local Loss: 0.0749 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 13.239s | Local Loss: 0.0794 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 12.692s | Local Loss: 0.2718 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 13.728s | Local Loss: 0.0200 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 14.442s | Local Loss: 0.1255 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 13.498s | Local Loss: 0.2350 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 14.111s | Local Loss: 0.0900 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 12.544s | Local Loss: 0.3449 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 13.505s | Local Loss: 0.0210 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 13.440s | Local Loss: 0.3120 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 12.141s | Local Loss: 0.1514 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 12.665s | Local Loss: 0.3181 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 12.414s | Local Loss: 0.0738 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 12.165s | Local Loss: 0.2079 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 13.690s | Local Loss: 0.0471 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 12.259s | Local Loss: 0.1131 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 13.417s | Local Loss: 0.5332 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 13.109s | Local Loss: 0.2410 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 12.124s | Local Loss: 0.0479 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 12.816s | Local Loss: 0.3056 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 13.823s | Local Loss: 0.0933 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 12.649s | Local Loss: 0.5354 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 12.736s | Local Loss: 0.3213 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 13.648s | Local Loss: 0.1464 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 12.352s | Local Loss: 0.1700 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 12.274s | Local Loss: 0.0908 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 13.098s | Local Loss: 0.0618 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 13.476s | Local Loss: 0.0936 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 13.373s | Local Loss: 0.3241 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 13.550s | Local Loss: 0.0553 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 12.958s | Local Loss: 0.0666 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 12.568s | Local Loss: 0.1299 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 12.575s | Local Loss: 0.0710 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 12.811s | Local Loss: 0.0501 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 12.567s | Local Loss: 0.3872 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 13.120s | Local Loss: 0.2298 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 12.753s | Local Loss: 0.2247 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 20.980s | Local Loss: 0.0829 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 17.446s | Local Loss: 0.1386 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 16.962s | Local Loss: 0.3457 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 17.400s | Local Loss: 0.0607 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 14.146s | Local Loss: 0.1107 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 13.561s | Local Loss: 0.1015 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 13.515s | Local Loss: 0.2254 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 14.114s | Local Loss: 0.1007 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 13.090s | Local Loss: 0.0533 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 13.280s | Local Loss: 0.3914 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 14.184s | Local Loss: 0.5303 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 15.087s | Local Loss: 0.2080 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 14.585s | Local Loss: 0.0404 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 13.720s | Local Loss: 0.0268 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 15.258s | Local Loss: 0.1519 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 14.643s | Local Loss: 0.0183 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 15.558s | Local Loss: 0.3751 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 13.582s | Local Loss: 0.0679 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 14.282s | Local Loss: 0.3877 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 14.201s | Local Loss: 0.4718 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 13.753s | Local Loss: 0.0415 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 13.511s | Local Loss: 0.0865 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 13.411s | Local Loss: 0.0831 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 14.166s | Local Loss: 0.3602 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 13.672s | Local Loss: 0.1404 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 14.640s | Local Loss: 0.2413 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 13.754s | Local Loss: 0.0263 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 13.916s | Local Loss: 0.0200 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 13.863s | Local Loss: 0.3665 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 13.379s | Local Loss: 0.0401 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 13.550s | Local Loss: 0.1067 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 13.643s | Local Loss: 0.0433 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 13.184s | Local Loss: 0.0724 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 14.028s | Local Loss: 0.0160 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 13.682s | Local Loss: 0.4212 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 12.917s | Local Loss: 0.0314 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 13.968s | Local Loss: 0.0739 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 13.463s | Local Loss: 0.1592 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 13.610s | Local Loss: 0.2248 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 13.374s | Local Loss: 0.0544 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 13.750s | Local Loss: 0.2125 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 13.592s | Local Loss: 0.1538 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 13.494s | Local Loss: 0.3302 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 14.101s | Local Loss: 0.1065 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 13.319s | Local Loss: 0.1180 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 13.447s | Local Loss: 0.1338 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 13.222s | Local Loss: 0.0687 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 14.184s | Local Loss: 0.2980 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 13.202s | Local Loss: 0.0380 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 13.559s | Local Loss: 0.1320 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 20.559s | Local Loss: 0.4052 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 17.117s | Local Loss: 0.1235 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 14.879s | Local Loss: 0.0137 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 14.990s | Local Loss: 0.0177 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 15.086s | Local Loss: 0.0497 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 16.246s | Local Loss: 0.4213 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 16.102s | Local Loss: 0.4235 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 14.747s | Local Loss: 0.1821 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 14.874s | Local Loss: 0.2556 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 16.328s | Local Loss: 0.1450 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 15.056s | Local Loss: 0.0508 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 16.215s | Local Loss: 0.3037 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 14.268s | Local Loss: 0.1943 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 13.383s | Local Loss: 0.0253 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 13.887s | Local Loss: 0.0784 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 13.748s | Local Loss: 0.2296 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 14.095s | Local Loss: 0.4734 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 13.819s | Local Loss: 0.0150 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 13.693s | Local Loss: 0.2187 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 13.795s | Local Loss: 0.0840 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 13.422s | Local Loss: 0.1424 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 13.812s | Local Loss: 0.1266 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 13.692s | Local Loss: 0.2693 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 13.772s | Local Loss: 0.1601 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 14.502s | Local Loss: 0.1010 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 13.333s | Local Loss: 0.0236 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 13.800s | Local Loss: 0.0631 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 13.736s | Local Loss: 0.0244 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 13.341s | Local Loss: 0.0790 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 14.742s | Local Loss: 0.2283 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 13.550s | Local Loss: 0.0000 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 13.989s | Local Loss: 0.0674 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 13.572s | Local Loss: 0.5174 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 13.461s | Local Loss: 0.0682 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 14.096s | Local Loss: 0.0216 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 14.219s | Local Loss: 0.0469 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 13.854s | Local Loss: 0.0745 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 14.563s | Local Loss: 0.0306 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 13.836s | Local Loss: 0.0341 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 14.654s | Local Loss: 0.0178 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 14.305s | Local Loss: 0.0527 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 14.624s | Local Loss: 0.0891 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 13.688s | Local Loss: 0.0636 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 13.754s | Local Loss: 0.2423 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 13.797s | Local Loss: 0.3379 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 13.972s | Local Loss: 0.2242 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 13.493s | Local Loss: 0.2096 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 13.904s | Local Loss: 0.0094 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 14.243s | Local Loss: 0.0260 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 13.732s | Local Loss: 0.1015 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 22.018s | Local Loss: 0.1709 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 16.894s | Local Loss: 0.1986 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 17.772s | Local Loss: 0.0706 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 18.097s | Local Loss: 0.0309 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 18.306s | Local Loss: 0.0541 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 17.180s | Local Loss: 0.0107 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 17.352s | Local Loss: 0.2755 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 18.312s | Local Loss: 0.3731 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 17.940s | Local Loss: 0.0794 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 17.068s | Local Loss: 0.2659 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 16.854s | Local Loss: 0.4247 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 16.578s | Local Loss: 0.1570 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 17.639s | Local Loss: 0.0668 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 17.612s | Local Loss: 0.0338 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 17.163s | Local Loss: 0.1212 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 16.731s | Local Loss: 0.0869 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 17.174s | Local Loss: 0.1550 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 18.291s | Local Loss: 0.0793 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 14.556s | Local Loss: 0.0347 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 13.871s | Local Loss: 0.1058 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 14.776s | Local Loss: 0.0630 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 14.252s | Local Loss: 0.0214 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 13.387s | Local Loss: 0.1128 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 14.155s | Local Loss: 0.0423 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 14.353s | Local Loss: 0.0358 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 13.578s | Local Loss: 0.0424 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 13.777s | Local Loss: 0.0336 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 13.842s | Local Loss: 0.0279 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 15.143s | Local Loss: 0.0299 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 16.037s | Local Loss: 0.0328 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 14.159s | Local Loss: 0.0339 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 14.621s | Local Loss: 0.0408 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 14.213s | Local Loss: 0.0557 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 14.081s | Local Loss: 0.1711 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 14.072s | Local Loss: 0.0617 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 14.102s | Local Loss: 0.0391 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 13.972s | Local Loss: 0.0311 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 14.002s | Local Loss: 0.2195 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 14.195s | Local Loss: 0.3746 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 13.837s | Local Loss: 0.0717 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 13.876s | Local Loss: 0.1302 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 13.891s | Local Loss: 0.4009 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 14.068s | Local Loss: 0.0893 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 16.835s | Local Loss: 0.0544 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 17.348s | Local Loss: 0.0242 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 16.379s | Local Loss: 0.0340 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 15.843s | Local Loss: 0.0332 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 16.204s | Local Loss: 0.0291 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 14.093s | Local Loss: 0.0279 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 14.593s | Local Loss: 0.0276 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 22.263s | Local Loss: 0.0569 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 17.675s | Local Loss: 0.1996 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 16.676s | Local Loss: 0.0689 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 18.484s | Local Loss: 0.0326 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 14.503s | Local Loss: 0.0358 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 14.024s | Local Loss: 0.0340 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 13.669s | Local Loss: 0.0370 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 14.996s | Local Loss: 0.4890 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 13.971s | Local Loss: 0.1351 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 13.647s | Local Loss: 0.0400 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 13.832s | Local Loss: 0.0199 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 22.906s | Local Loss: 0.0365 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 21.216s | Local Loss: 0.1889 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 14.610s | Local Loss: 0.0369 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 14.397s | Local Loss: 0.0168 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 16.998s | Local Loss: 0.0662 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 14.536s | Local Loss: 0.0925 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 15.487s | Local Loss: 0.1191 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 14.054s | Local Loss: 0.0320 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 15.590s | Local Loss: 0.0207 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 13.722s | Local Loss: 0.0150 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 14.276s | Local Loss: 0.0223 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 14.138s | Local Loss: 0.0549 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 14.396s | Local Loss: 0.1027 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 15.221s | Local Loss: 0.0390 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 14.148s | Local Loss: 0.0575 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 13.928s | Local Loss: 0.0000 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 14.085s | Local Loss: 0.0798 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 14.613s | Local Loss: 0.1925 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 14.935s | Local Loss: 0.0551 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 14.148s | Local Loss: 0.0216 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 14.706s | Local Loss: 0.0202 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 13.805s | Local Loss: 0.3237 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 14.450s | Local Loss: 0.0449 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 13.909s | Local Loss: 0.2955 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 14.069s | Local Loss: 0.2549 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 14.715s | Local Loss: 0.0318 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 13.818s | Local Loss: 0.0340 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 14.793s | Local Loss: 0.3053 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 14.402s | Local Loss: 0.1457 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 14.358s | Local Loss: 0.0377 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 14.728s | Local Loss: 0.2726 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 13.744s | Local Loss: 0.0171 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 14.253s | Local Loss: 0.1803 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 14.788s | Local Loss: 0.0387 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 14.314s | Local Loss: 0.0427 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 15.096s | Local Loss: 0.0082 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 13.553s | Local Loss: 0.2606 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 15.160s | Local Loss: 0.0042 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 14.250s | Local Loss: 0.0240 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 22.018s | Local Loss: 0.0129 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 16.119s | Local Loss: 0.1654 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 15.089s | Local Loss: 0.2987 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 17.058s | Local Loss: 0.0256 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 14.794s | Local Loss: 0.3814 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 15.635s | Local Loss: 0.0340 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 15.054s | Local Loss: 0.2199 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 16.216s | Local Loss: 0.1585 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 16.403s | Local Loss: 0.3692 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 13.615s | Local Loss: 0.0546 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 15.044s | Local Loss: 0.1340 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 13.514s | Local Loss: 0.0354 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 13.479s | Local Loss: 0.0443 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 14.855s | Local Loss: 0.0325 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 13.513s | Local Loss: 0.4109 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 14.706s | Local Loss: 0.0230 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 13.912s | Local Loss: 0.0212 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 13.519s | Local Loss: 0.1543 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 14.999s | Local Loss: 0.1583 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 13.527s | Local Loss: 0.2246 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 13.497s | Local Loss: 0.0250 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 14.330s | Local Loss: 0.0318 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 13.769s | Local Loss: 0.0332 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 14.348s | Local Loss: 0.0667 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 13.708s | Local Loss: 0.0178 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 13.693s | Local Loss: 0.0184 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 14.695s | Local Loss: 0.0106 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 13.620s | Local Loss: 0.0441 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 14.972s | Local Loss: 0.0260 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 14.311s | Local Loss: 0.1180 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 13.710s | Local Loss: 0.0253 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 15.438s | Local Loss: 0.0180 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 13.595s | Local Loss: 0.0210 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 14.010s | Local Loss: 0.0404 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 15.888s | Local Loss: 0.3831 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 13.285s | Local Loss: 0.0622 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 14.102s | Local Loss: 0.1525 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 14.257s | Local Loss: 0.1137 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 14.159s | Local Loss: 0.0579 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 14.022s | Local Loss: 0.3264 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 13.640s | Local Loss: 0.0610 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 14.294s | Local Loss: 0.0427 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 14.636s | Local Loss: 0.0140 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 14.232s | Local Loss: 0.4368 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 13.910s | Local Loss: 0.0354 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 13.746s | Local Loss: 0.0212 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 13.893s | Local Loss: 0.1943 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 14.666s | Local Loss: 0.2226 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 14.354s | Local Loss: 0.0242 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 14.205s | Local Loss: 0.0161 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 24.566s | Local Loss: 0.2063 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 19.179s | Local Loss: 0.1423 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 17.307s | Local Loss: 0.0851 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 19.263s | Local Loss: 0.0331 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 18.909s | Local Loss: 0.0865 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 20.566s | Local Loss: 0.0275 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 17.656s | Local Loss: 0.0110 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 19.059s | Local Loss: 0.0254 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 17.592s | Local Loss: 0.2882 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 18.624s | Local Loss: 0.1782 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 17.708s | Local Loss: 0.0105 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 17.443s | Local Loss: 0.0280 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 17.630s | Local Loss: 0.0179 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 17.333s | Local Loss: 0.0265 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 18.618s | Local Loss: 0.0314 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 13.600s | Local Loss: 0.1037 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 13.938s | Local Loss: 0.0147 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 14.468s | Local Loss: 0.1676 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 14.643s | Local Loss: 0.0340 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 14.276s | Local Loss: 0.1046 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 13.617s | Local Loss: 0.1251 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 13.677s | Local Loss: 0.0121 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 14.466s | Local Loss: 0.0248 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 16.933s | Local Loss: 0.0186 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 15.914s | Local Loss: 0.0442 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 15.488s | Local Loss: 0.0080 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 17.155s | Local Loss: 0.0228 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 14.850s | Local Loss: 0.0296 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 14.338s | Local Loss: 0.1401 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 14.869s | Local Loss: 0.3097 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 15.111s | Local Loss: 0.0082 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 15.129s | Local Loss: 0.0449 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 14.858s | Local Loss: 0.0317 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 13.807s | Local Loss: 0.0179 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 15.299s | Local Loss: 0.0216 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 14.032s | Local Loss: 0.0309 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 14.510s | Local Loss: 0.0153 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 14.809s | Local Loss: 0.1624 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 14.696s | Local Loss: 0.0358 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 16.656s | Local Loss: 0.0079 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 15.961s | Local Loss: 0.0350 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 15.514s | Local Loss: 0.0135 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 14.810s | Local Loss: 0.0109 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 14.698s | Local Loss: 0.0084 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 14.458s | Local Loss: 0.0100 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 13.857s | Local Loss: 0.0176 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 14.429s | Local Loss: 0.0235 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 14.102s | Local Loss: 0.0334 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 14.657s | Local Loss: 0.0175 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 14.053s | Local Loss: 0.0273 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 19.461s | Local Loss: 0.0206 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 17.106s | Local Loss: 0.0141 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 17.754s | Local Loss: 0.0158 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 16.811s | Local Loss: 0.0155 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 17.489s | Local Loss: 0.0090 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 16.835s | Local Loss: 0.0031 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 15.382s | Local Loss: 0.0000 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 16.817s | Local Loss: 0.1439 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 14.825s | Local Loss: 0.0113 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 19.635s | Local Loss: 0.0181 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 24.521s | Local Loss: 0.1801 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 17.324s | Local Loss: 0.0169 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 15.378s | Local Loss: 0.0232 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 15.341s | Local Loss: 0.0979 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 15.563s | Local Loss: 0.0978 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 15.231s | Local Loss: 0.0440 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 15.764s | Local Loss: 0.0210 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 15.688s | Local Loss: 0.2176 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 15.501s | Local Loss: 0.0119 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 13.865s | Local Loss: 0.0638 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 13.463s | Local Loss: 0.0980 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 13.204s | Local Loss: 0.0176 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 14.023s | Local Loss: 0.0093 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 14.470s | Local Loss: 0.0150 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 14.946s | Local Loss: 0.0419 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 14.482s | Local Loss: 0.0335 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 14.076s | Local Loss: 0.0103 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 13.697s | Local Loss: 0.0268 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 13.362s | Local Loss: 0.0117 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 15.187s | Local Loss: 0.0100 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 13.337s | Local Loss: 0.0108 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 13.886s | Local Loss: 0.0210 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 13.505s | Local Loss: 0.1498 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 13.481s | Local Loss: 0.0195 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 14.701s | Local Loss: 0.0551 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 13.391s | Local Loss: 0.1219 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 13.134s | Local Loss: 0.0186 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 14.598s | Local Loss: 0.0312 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 13.636s | Local Loss: 0.0214 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 13.777s | Local Loss: 0.0389 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 13.845s | Local Loss: 0.0290 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 13.677s | Local Loss: 0.0491 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 14.390s | Local Loss: 0.0164 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 13.537s | Local Loss: 0.2635 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 13.655s | Local Loss: 0.2718 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 14.150s | Local Loss: 0.0414 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 13.458s | Local Loss: 0.0480 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 14.638s | Local Loss: 0.0402 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 13.248s | Local Loss: 0.0733 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 13.628s | Local Loss: 0.0158 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 21.926s | Local Loss: 0.0193 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 17.697s | Local Loss: 0.0073 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 17.262s | Local Loss: 0.1332 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 17.636s | Local Loss: 0.0186 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 17.260s | Local Loss: 0.0027 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 18.004s | Local Loss: 0.0132 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 18.529s | Local Loss: 0.1462 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 14.791s | Local Loss: 0.1429 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 13.331s | Local Loss: 0.0332 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 14.544s | Local Loss: 0.0187 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 13.795s | Local Loss: 0.0341 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 13.717s | Local Loss: 0.0064 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 13.560s | Local Loss: 0.0107 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 14.242s | Local Loss: 0.0145 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 14.083s | Local Loss: 0.0533 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 13.594s | Local Loss: 0.0352 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 14.302s | Local Loss: 0.0468 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 13.863s | Local Loss: 0.0122 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 13.208s | Local Loss: 0.0099 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 14.400s | Local Loss: 0.1992 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 14.215s | Local Loss: 0.0514 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 13.407s | Local Loss: 0.1060 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 13.496s | Local Loss: 0.0273 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 13.177s | Local Loss: 0.0061 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 13.636s | Local Loss: 0.0325 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 13.930s | Local Loss: 0.0197 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 13.248s | Local Loss: 0.0162 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 13.421s | Local Loss: 0.0311 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 13.875s | Local Loss: 0.0000 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 13.972s | Local Loss: 0.0194 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 13.905s | Local Loss: 0.0137 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 13.975s | Local Loss: 0.0134 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 14.380s | Local Loss: 0.0230 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 13.744s | Local Loss: 0.0323 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 14.751s | Local Loss: 0.0200 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 13.359s | Local Loss: 0.0132 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 13.825s | Local Loss: 0.0080 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 13.829s | Local Loss: 0.0118 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 13.807s | Local Loss: 0.2529 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 14.002s | Local Loss: 0.0270 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 13.238s | Local Loss: 0.0449 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 14.222s | Local Loss: 0.0134 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 14.166s | Local Loss: 0.0115 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 13.509s | Local Loss: 0.0786 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 14.152s | Local Loss: 0.0118 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 13.608s | Local Loss: 0.0213 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 14.404s | Local Loss: 0.0187 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 13.695s | Local Loss: 0.0130 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 13.903s | Local Loss: 0.0138 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 14.807s | Local Loss: 0.0127 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 21.601s | Local Loss: 0.0420 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 17.727s | Local Loss: 0.0366 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 16.705s | Local Loss: 0.0195 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 17.377s | Local Loss: 0.0088 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 17.464s | Local Loss: 0.0093 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 17.016s | Local Loss: 0.0080 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 18.829s | Local Loss: 0.0082 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 18.883s | Local Loss: 0.0100 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 18.525s | Local Loss: 0.0080 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 17.528s | Local Loss: 0.0132 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 18.024s | Local Loss: 0.0369 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 18.533s | Local Loss: 0.0220 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 14.709s | Local Loss: 0.2659 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 13.829s | Local Loss: 0.0245 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 13.875s | Local Loss: 0.0215 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 14.365s | Local Loss: 0.0123 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 13.595s | Local Loss: 0.1508 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 13.826s | Local Loss: 0.0119 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 15.784s | Local Loss: 0.0159 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 13.492s | Local Loss: 0.0387 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 14.105s | Local Loss: 0.0000 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 14.224s | Local Loss: 0.0853 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 13.746s | Local Loss: 0.1947 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 14.434s | Local Loss: 0.0162 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 13.369s | Local Loss: 0.0069 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 14.095s | Local Loss: 0.0173 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 13.995s | Local Loss: 0.0088 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 14.828s | Local Loss: 0.0125 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 13.479s | Local Loss: 0.2924 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 14.507s | Local Loss: 0.0587 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 15.005s | Local Loss: 0.0134 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 14.311s | Local Loss: 0.0100 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 14.620s | Local Loss: 0.0188 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 14.822s | Local Loss: 0.0084 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 14.591s | Local Loss: 0.0100 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 13.997s | Local Loss: 0.0196 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 14.992s | Local Loss: 0.0302 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 14.745s | Local Loss: 0.0072 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 13.862s | Local Loss: 0.0120 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 14.542s | Local Loss: 0.0138 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 14.527s | Local Loss: 0.0080 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 13.680s | Local Loss: 0.0203 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 13.371s | Local Loss: 0.0062 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 15.876s | Local Loss: 0.0104 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 13.389s | Local Loss: 0.0108 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 13.852s | Local Loss: 0.0063 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 13.981s | Local Loss: 0.0054 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 13.784s | Local Loss: 0.0161 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 14.370s | Local Loss: 0.0085 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 13.581s | Local Loss: 0.0121 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 22:41:07] Logger closing.
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 16.110s | Local Loss: 0.5407 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 13.794s | Local Loss: 0.3472 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.169s | Local Loss: 0.1271 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.140s | Local Loss: 0.1963 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 12.722s | Local Loss: 0.1666 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 11.922s | Local Loss: 0.2431 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.084s | Local Loss: 0.0569 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 12.351s | Local Loss: 0.3557 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.705s | Local Loss: 0.2418 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 15.140s | Local Loss: 0.0994 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 14.091s | Local Loss: 0.0086 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.232s | Local Loss: 0.3490 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 13.627s | Local Loss: 0.5304 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.147s | Local Loss: 0.0757 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 12.742s | Local Loss: 0.0794 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.196s | Local Loss: 0.2719 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 11.807s | Local Loss: 0.0199 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 12.061s | Local Loss: 0.1256 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 11.857s | Local Loss: 0.2351 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 11.873s | Local Loss: 0.0917 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 12.825s | Local Loss: 0.3452 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 11.673s | Local Loss: 0.0210 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 12.859s | Local Loss: 0.3124 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 11.934s | Local Loss: 0.1518 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.146s | Local Loss: 0.3184 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.418s | Local Loss: 0.0737 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 11.847s | Local Loss: 0.2086 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 11.578s | Local Loss: 0.0467 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.209s | Local Loss: 0.1153 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.242s | Local Loss: 0.5343 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.445s | Local Loss: 0.2423 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 13.066s | Local Loss: 0.0486 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 11.407s | Local Loss: 0.3067 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 11.507s | Local Loss: 0.0934 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.268s | Local Loss: 0.5363 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.244s | Local Loss: 0.3218 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 11.551s | Local Loss: 0.1472 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.226s | Local Loss: 0.1705 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 11.775s | Local Loss: 0.0918 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 11.620s | Local Loss: 0.0638 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 12.064s | Local Loss: 0.0941 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.450s | Local Loss: 0.3252 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 11.975s | Local Loss: 0.0559 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 12.951s | Local Loss: 0.0685 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 11.700s | Local Loss: 0.1314 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 11.756s | Local Loss: 0.0706 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 11.944s | Local Loss: 0.0506 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 11.832s | Local Loss: 0.3880 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 11.711s | Local Loss: 0.2302 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 11.835s | Local Loss: 0.2246 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 20.870s | Local Loss: 0.0825 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 15.455s | Local Loss: 0.1399 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 15.568s | Local Loss: 0.3471 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 13.797s | Local Loss: 0.0605 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 13.192s | Local Loss: 0.1121 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 13.785s | Local Loss: 0.1007 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 14.686s | Local Loss: 0.2264 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 13.250s | Local Loss: 0.1010 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 13.054s | Local Loss: 0.0532 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 13.540s | Local Loss: 0.3926 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 13.401s | Local Loss: 0.5302 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 12.993s | Local Loss: 0.2095 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 13.471s | Local Loss: 0.0407 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 12.909s | Local Loss: 0.0298 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 12.883s | Local Loss: 0.1532 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 13.201s | Local Loss: 0.0181 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 12.911s | Local Loss: 0.3749 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 13.167s | Local Loss: 0.0678 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 12.599s | Local Loss: 0.3896 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 12.933s | Local Loss: 0.4728 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 12.774s | Local Loss: 0.0423 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 13.010s | Local Loss: 0.0869 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 13.067s | Local Loss: 0.0842 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 13.144s | Local Loss: 0.3616 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 12.948s | Local Loss: 0.1405 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 13.229s | Local Loss: 0.2414 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 12.907s | Local Loss: 0.0256 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 13.069s | Local Loss: 0.0205 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 13.407s | Local Loss: 0.3662 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 13.027s | Local Loss: 0.0405 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 12.845s | Local Loss: 0.1073 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 12.757s | Local Loss: 0.0439 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 12.710s | Local Loss: 0.0760 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 13.183s | Local Loss: 0.0165 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 12.875s | Local Loss: 0.4225 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 12.696s | Local Loss: 0.0317 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 13.332s | Local Loss: 0.0740 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 12.802s | Local Loss: 0.1605 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 12.895s | Local Loss: 0.2264 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 12.794s | Local Loss: 0.0589 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 12.662s | Local Loss: 0.2133 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 13.392s | Local Loss: 0.1548 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 12.797s | Local Loss: 0.3321 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 13.064s | Local Loss: 0.1084 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 13.131s | Local Loss: 0.1187 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 12.972s | Local Loss: 0.1357 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 13.141s | Local Loss: 0.0758 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 12.728s | Local Loss: 0.3014 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 12.687s | Local Loss: 0.0378 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 13.121s | Local Loss: 0.1351 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 19.998s | Local Loss: 0.4073 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 16.932s | Local Loss: 0.1245 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 17.910s | Local Loss: 0.0142 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 16.180s | Local Loss: 0.0173 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 16.814s | Local Loss: 0.0500 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 17.347s | Local Loss: 0.4220 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 16.987s | Local Loss: 0.4237 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 18.168s | Local Loss: 0.1841 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 13.410s | Local Loss: 0.2556 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 14.167s | Local Loss: 0.1467 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 13.351s | Local Loss: 0.0556 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 13.684s | Local Loss: 0.3068 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 13.428s | Local Loss: 0.1986 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 13.064s | Local Loss: 0.0257 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 13.555s | Local Loss: 0.0798 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 13.392s | Local Loss: 0.2311 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 13.373s | Local Loss: 0.4750 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 13.600s | Local Loss: 0.0154 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 14.174s | Local Loss: 0.2213 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 13.443s | Local Loss: 0.0857 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 13.687s | Local Loss: 0.1443 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 13.608s | Local Loss: 0.1284 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 13.091s | Local Loss: 0.2741 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 13.240s | Local Loss: 0.1617 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 13.238s | Local Loss: 0.1021 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 14.223s | Local Loss: 0.0231 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 13.600s | Local Loss: 0.0645 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 13.200s | Local Loss: 0.0249 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 13.782s | Local Loss: 0.0801 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 13.679s | Local Loss: 0.2301 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 13.315s | Local Loss: 0.0000 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 13.535s | Local Loss: 0.0666 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 13.640s | Local Loss: 0.5189 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 13.856s | Local Loss: 0.0686 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 13.397s | Local Loss: 0.0223 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 12.844s | Local Loss: 0.0467 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 13.696s | Local Loss: 0.0748 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 13.361s | Local Loss: 0.0318 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 13.305s | Local Loss: 0.0344 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 13.682s | Local Loss: 0.0181 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 13.810s | Local Loss: 0.0536 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 13.854s | Local Loss: 0.0909 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 13.815s | Local Loss: 0.0642 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 13.097s | Local Loss: 0.2439 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 13.944s | Local Loss: 0.3378 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 13.301s | Local Loss: 0.2268 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 13.445s | Local Loss: 0.2117 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 13.202s | Local Loss: 0.0098 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 13.525s | Local Loss: 0.0265 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 13.627s | Local Loss: 0.1025 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 19.589s | Local Loss: 0.1732 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 16.855s | Local Loss: 0.2008 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 17.858s | Local Loss: 0.0711 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 16.615s | Local Loss: 0.0282 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 17.773s | Local Loss: 0.0544 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 16.434s | Local Loss: 0.0101 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 17.166s | Local Loss: 0.2774 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 17.390s | Local Loss: 0.3764 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 17.962s | Local Loss: 0.0805 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 16.626s | Local Loss: 0.2679 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 19.133s | Local Loss: 0.4274 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 13.204s | Local Loss: 0.1582 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 14.135s | Local Loss: 0.0674 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 13.631s | Local Loss: 0.0347 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 13.666s | Local Loss: 0.1219 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 13.521s | Local Loss: 0.0872 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 13.036s | Local Loss: 0.1575 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 13.656s | Local Loss: 0.0802 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 13.439s | Local Loss: 0.0355 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 13.214s | Local Loss: 0.1070 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 13.574s | Local Loss: 0.0646 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 13.132s | Local Loss: 0.0217 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 13.355s | Local Loss: 0.1153 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 13.115s | Local Loss: 0.0490 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 13.303s | Local Loss: 0.0361 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 13.705s | Local Loss: 0.0433 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 13.511s | Local Loss: 0.0359 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 13.042s | Local Loss: 0.0275 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 13.804s | Local Loss: 0.0342 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 13.578s | Local Loss: 0.0348 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 13.415s | Local Loss: 0.0346 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 13.553s | Local Loss: 0.0415 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 13.733s | Local Loss: 0.0555 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 13.622s | Local Loss: 0.1740 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 13.729s | Local Loss: 0.0626 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 13.379s | Local Loss: 0.0399 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 13.581s | Local Loss: 0.0316 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 13.411s | Local Loss: 0.2227 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 13.333s | Local Loss: 0.3780 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 13.652s | Local Loss: 0.0732 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 13.583s | Local Loss: 0.1320 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 13.297s | Local Loss: 0.4041 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 13.684s | Local Loss: 0.0897 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 13.567s | Local Loss: 0.0548 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 13.590s | Local Loss: 0.0243 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 13.147s | Local Loss: 0.0343 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 13.321s | Local Loss: 0.0335 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 13.617s | Local Loss: 0.0314 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 13.767s | Local Loss: 0.0182 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 13.218s | Local Loss: 0.0237 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 17.853s | Local Loss: 0.0563 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 15.261s | Local Loss: 0.2007 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 15.044s | Local Loss: 0.0678 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 15.181s | Local Loss: 0.0336 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 15.348s | Local Loss: 0.0362 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 14.904s | Local Loss: 0.0334 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 15.511s | Local Loss: 0.0371 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 15.375s | Local Loss: 0.4880 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 14.718s | Local Loss: 0.1358 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 14.892s | Local Loss: 0.0404 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 15.436s | Local Loss: 0.0175 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 15.185s | Local Loss: 0.0362 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 14.953s | Local Loss: 0.1900 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 15.240s | Local Loss: 0.0374 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 15.248s | Local Loss: 0.0184 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 15.188s | Local Loss: 0.0668 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 14.691s | Local Loss: 0.0927 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 16.450s | Local Loss: 0.1211 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 13.455s | Local Loss: 0.0321 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 13.626s | Local Loss: 0.0243 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 13.482s | Local Loss: 0.0167 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 13.565s | Local Loss: 0.0235 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 13.374s | Local Loss: 0.0562 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 13.640s | Local Loss: 0.1040 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 13.268s | Local Loss: 0.0370 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 13.618s | Local Loss: 0.0583 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 13.463s | Local Loss: 0.0000 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 13.491s | Local Loss: 0.0803 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 13.364s | Local Loss: 0.1945 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 13.352s | Local Loss: 0.0558 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 13.185s | Local Loss: 0.0217 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 13.347s | Local Loss: 0.0222 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 13.674s | Local Loss: 0.3264 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 13.599s | Local Loss: 0.0599 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 13.330s | Local Loss: 0.2972 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 13.668s | Local Loss: 0.2637 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 13.422s | Local Loss: 0.0336 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 13.674s | Local Loss: 0.0362 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 13.842s | Local Loss: 0.3135 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 13.267s | Local Loss: 0.1500 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 13.329s | Local Loss: 0.0393 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 13.825s | Local Loss: 0.2771 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 13.398s | Local Loss: 0.0188 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 13.304s | Local Loss: 0.1813 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 13.837s | Local Loss: 0.0397 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 13.620s | Local Loss: 0.0447 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 13.470s | Local Loss: 0.0082 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 13.686s | Local Loss: 0.2641 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 13.552s | Local Loss: 0.0062 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 13.342s | Local Loss: 0.0252 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 19.858s | Local Loss: 0.0119 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 17.037s | Local Loss: 0.1684 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 17.605s | Local Loss: 0.3051 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 17.129s | Local Loss: 0.0261 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 17.430s | Local Loss: 0.3869 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 18.459s | Local Loss: 0.0358 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 14.344s | Local Loss: 0.2292 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 13.678s | Local Loss: 0.1612 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 13.540s | Local Loss: 0.3753 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 13.918s | Local Loss: 0.0562 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 13.383s | Local Loss: 0.1363 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 13.623s | Local Loss: 0.0355 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 13.501s | Local Loss: 0.0450 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 14.000s | Local Loss: 0.0330 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 13.610s | Local Loss: 0.4116 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 13.370s | Local Loss: 0.0250 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 13.279s | Local Loss: 0.0201 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 14.437s | Local Loss: 0.1556 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 13.087s | Local Loss: 0.1603 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 13.225s | Local Loss: 0.2277 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 13.405s | Local Loss: 0.0254 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 13.358s | Local Loss: 0.0324 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 21.115s | Local Loss: 0.0344 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 20.702s | Local Loss: 0.0681 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 22.635s | Local Loss: 0.0182 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 20.544s | Local Loss: 0.0161 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 20.396s | Local Loss: 0.0109 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 21.024s | Local Loss: 0.0444 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 24.816s | Local Loss: 0.0265 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 20.215s | Local Loss: 0.1195 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 20.813s | Local Loss: 0.0250 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 20.253s | Local Loss: 0.0241 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 21.577s | Local Loss: 0.0194 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 20.237s | Local Loss: 0.0407 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 20.881s | Local Loss: 0.3852 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 24.015s | Local Loss: 0.0635 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 21.529s | Local Loss: 0.1517 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 20.071s | Local Loss: 0.1142 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 22.291s | Local Loss: 0.0577 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 20.855s | Local Loss: 0.3287 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 25.013s | Local Loss: 0.0616 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 20.028s | Local Loss: 0.0493 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 21.246s | Local Loss: 0.0162 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 20.875s | Local Loss: 0.4381 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 21.350s | Local Loss: 0.0363 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 16.038s | Local Loss: 0.0219 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 13.196s | Local Loss: 0.1960 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 13.429s | Local Loss: 0.2282 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 13.634s | Local Loss: 0.0241 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 13.326s | Local Loss: 0.0124 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 17.919s | Local Loss: 0.2245 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 15.586s | Local Loss: 0.1407 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 15.397s | Local Loss: 0.0893 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 15.158s | Local Loss: 0.0373 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 14.738s | Local Loss: 0.0881 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 15.466s | Local Loss: 0.0270 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 15.199s | Local Loss: 0.0110 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 15.276s | Local Loss: 0.0255 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 14.891s | Local Loss: 0.2884 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 14.848s | Local Loss: 0.1781 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 15.920s | Local Loss: 0.0090 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 13.576s | Local Loss: 0.0276 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 13.483s | Local Loss: 0.0173 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 13.314s | Local Loss: 0.0270 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 13.169s | Local Loss: 0.0387 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 13.417s | Local Loss: 0.1051 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 13.828s | Local Loss: 0.0144 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 13.334s | Local Loss: 0.1685 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 13.364s | Local Loss: 0.0333 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 13.619s | Local Loss: 0.1040 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 13.586s | Local Loss: 0.1240 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 14.114s | Local Loss: 0.0120 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 14.471s | Local Loss: 0.0247 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 14.274s | Local Loss: 0.0197 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 14.232s | Local Loss: 0.0439 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 14.347s | Local Loss: 0.0079 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 14.272s | Local Loss: 0.0227 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 13.492s | Local Loss: 0.0289 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 13.887s | Local Loss: 0.1402 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 13.369s | Local Loss: 0.3115 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 13.836s | Local Loss: 0.0081 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 13.455s | Local Loss: 0.0437 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 13.838s | Local Loss: 0.0314 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 13.652s | Local Loss: 0.0176 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 13.714s | Local Loss: 0.0237 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 13.487s | Local Loss: 0.0282 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 14.162s | Local Loss: 0.0138 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 13.324s | Local Loss: 0.1630 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 13.430s | Local Loss: 0.0357 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 14.192s | Local Loss: 0.0100 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 13.340s | Local Loss: 0.0343 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 13.352s | Local Loss: 0.0133 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 13.322s | Local Loss: 0.0103 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 13.659s | Local Loss: 0.0089 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 13.842s | Local Loss: 0.0115 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 14.184s | Local Loss: 0.0160 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 13.708s | Local Loss: 0.0234 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 14.097s | Local Loss: 0.0329 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 13.533s | Local Loss: 0.0188 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 13.644s | Local Loss: 0.0254 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 19.967s | Local Loss: 0.0229 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 17.518s | Local Loss: 0.0151 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 16.741s | Local Loss: 0.0156 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 17.934s | Local Loss: 0.0152 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 17.079s | Local Loss: 0.0102 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 19.130s | Local Loss: 0.0044 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 17.255s | Local Loss: 0.0000 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 18.056s | Local Loss: 0.1447 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 17.314s | Local Loss: 0.0115 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 17.607s | Local Loss: 0.0182 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 18.080s | Local Loss: 0.1825 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 18.312s | Local Loss: 0.0185 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 17.011s | Local Loss: 0.0246 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 25.204s | Local Loss: 0.1004 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 20.362s | Local Loss: 0.0994 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 18.843s | Local Loss: 0.0458 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 14.267s | Local Loss: 0.0221 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 13.550s | Local Loss: 0.2205 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 12.972s | Local Loss: 0.0127 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 13.144s | Local Loss: 0.0650 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 13.171s | Local Loss: 0.0996 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 12.711s | Local Loss: 0.0188 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 13.182s | Local Loss: 0.0125 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 12.900s | Local Loss: 0.0136 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 12.997s | Local Loss: 0.0452 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 13.173s | Local Loss: 0.0341 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 12.998s | Local Loss: 0.0131 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 13.165s | Local Loss: 0.0277 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 13.304s | Local Loss: 0.0120 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 12.736s | Local Loss: 0.0121 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 13.077s | Local Loss: 0.0096 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 12.942s | Local Loss: 0.0218 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 13.189s | Local Loss: 0.1524 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 12.782s | Local Loss: 0.0205 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 12.903s | Local Loss: 0.0567 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 13.054s | Local Loss: 0.1228 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 12.583s | Local Loss: 0.0098 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 12.994s | Local Loss: 0.0321 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 12.883s | Local Loss: 0.0228 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 13.002s | Local Loss: 0.0412 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 12.974s | Local Loss: 0.0300 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 13.721s | Local Loss: 0.0526 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 16.242s | Local Loss: 0.0174 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 14.097s | Local Loss: 0.2796 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 14.103s | Local Loss: 0.2744 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 13.282s | Local Loss: 0.0445 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 14.191s | Local Loss: 0.0519 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_2.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_2.log
new file mode 100644
index 0000000000000000000000000000000000000000..85d40907968d11f3566bb9acb8cf4f635df83049
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_2.log
@@ -0,0 +1,900 @@
+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 17.508s | Local Loss: 0.1878 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 14.202s | Local Loss: 0.0850 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 13.442s | Local Loss: 0.5211 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 12.803s | Local Loss: 0.4083 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 12.055s | Local Loss: 0.0465 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 14.292s | Local Loss: 0.1060 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 21.685s | Local Loss: 0.3310 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 13.390s | Local Loss: 0.2997 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 13.741s | Local Loss: 0.1698 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 12.584s | Local Loss: 0.1502 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 13.254s | Local Loss: 0.1625 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 13.456s | Local Loss: 0.0707 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 14.247s | Local Loss: 0.2276 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 14.464s | Local Loss: 0.3893 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 12.634s | Local Loss: 0.4411 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 12.349s | Local Loss: 0.0267 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 13.232s | Local Loss: 0.0454 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 14.000s | Local Loss: 0.2185 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 12.809s | Local Loss: 0.2021 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 13.520s | Local Loss: 0.0904 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 11.941s | Local Loss: 0.0852 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 13.008s | Local Loss: 0.0645 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 13.031s | Local Loss: 0.0103 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 11.567s | Local Loss: 0.0377 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 12.110s | Local Loss: 0.1045 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 11.843s | Local Loss: 0.1036 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 11.557s | Local Loss: 0.2604 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 13.096s | Local Loss: 0.0961 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 11.823s | Local Loss: 0.2191 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 12.966s | Local Loss: 0.4110 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 12.481s | Local Loss: 0.0781 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 11.812s | Local Loss: 0.0697 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 12.149s | Local Loss: 0.0502 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 13.300s | Local Loss: 0.2265 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 12.357s | Local Loss: 0.1436 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 12.132s | Local Loss: 0.0379 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 13.164s | Local Loss: 0.0155 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 11.782s | Local Loss: 0.3756 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 11.676s | Local Loss: 0.1434 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 12.450s | Local Loss: 0.3718 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 13.049s | Local Loss: 0.2162 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 12.681s | Local Loss: 0.1174 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 12.888s | Local Loss: 0.0865 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 12.438s | Local Loss: 0.0797 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 12.111s | Local Loss: 0.5673 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 12.003s | Local Loss: 0.1599 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 12.291s | Local Loss: 0.1202 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 12.089s | Local Loss: 0.1633 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 12.531s | Local Loss: 0.0587 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 12.325s | Local Loss: 0.1348 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 20.410s | Local Loss: 0.0072 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 16.867s | Local Loss: 0.0589 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 16.363s | Local Loss: 0.0247 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 16.894s | Local Loss: 0.0151 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 13.568s | Local Loss: 0.0748 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 13.085s | Local Loss: 0.0744 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 13.144s | Local Loss: 0.1159 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 13.422s | Local Loss: 0.0317 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 12.585s | Local Loss: 0.3263 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 12.794s | Local Loss: 0.2603 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 13.554s | Local Loss: 0.1288 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 14.438s | Local Loss: 0.0729 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 13.958s | Local Loss: 0.0545 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 13.436s | Local Loss: 0.4417 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 14.512s | Local Loss: 0.1255 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 13.872s | Local Loss: 0.3717 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 14.977s | Local Loss: 0.3403 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 12.966s | Local Loss: 0.1407 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 13.903s | Local Loss: 0.1528 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 13.530s | Local Loss: 0.3593 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 13.243s | Local Loss: 0.0962 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 12.890s | Local Loss: 0.0718 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 12.905s | Local Loss: 0.0287 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 13.678s | Local Loss: 0.3611 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 12.977s | Local Loss: 0.0568 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 13.947s | Local Loss: 0.0486 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 13.250s | Local Loss: 0.0923 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 13.221s | Local Loss: 0.0498 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 13.419s | Local Loss: 0.2169 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 12.680s | Local Loss: 0.1212 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 12.825s | Local Loss: 0.1372 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 13.136s | Local Loss: 0.2300 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 12.598s | Local Loss: 0.2087 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 13.147s | Local Loss: 0.0363 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 12.948s | Local Loss: 0.0664 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 12.259s | Local Loss: 0.0143 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 13.395s | Local Loss: 0.1406 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 12.750s | Local Loss: 0.0974 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 13.012s | Local Loss: 0.1206 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 12.694s | Local Loss: 0.1462 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 13.020s | Local Loss: 0.0727 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 13.155s | Local Loss: 0.0813 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 12.881s | Local Loss: 0.1056 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 13.369s | Local Loss: 0.0535 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 12.772s | Local Loss: 0.0539 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 12.815s | Local Loss: 0.0289 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 12.738s | Local Loss: 0.1093 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 13.593s | Local Loss: 0.0275 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 12.627s | Local Loss: 0.0232 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 12.912s | Local Loss: 0.1262 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 19.775s | Local Loss: 0.0764 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 16.664s | Local Loss: 0.2991 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 14.193s | Local Loss: 0.0303 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 14.421s | Local Loss: 0.0614 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 14.592s | Local Loss: 0.5389 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 15.686s | Local Loss: 0.1733 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 15.633s | Local Loss: 0.0671 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 14.200s | Local Loss: 0.2623 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 14.318s | Local Loss: 0.0434 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 15.716s | Local Loss: 0.1880 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 14.535s | Local Loss: 0.0370 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 15.586s | Local Loss: 0.0680 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 13.652s | Local Loss: 0.1290 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 12.963s | Local Loss: 0.0702 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 13.236s | Local Loss: 0.0879 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 13.011s | Local Loss: 0.0954 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 13.671s | Local Loss: 0.1766 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 13.263s | Local Loss: 0.2833 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 13.213s | Local Loss: 0.1115 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 13.408s | Local Loss: 0.0310 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 12.906s | Local Loss: 0.0479 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 13.331s | Local Loss: 0.0814 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 13.201s | Local Loss: 0.0232 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 13.199s | Local Loss: 0.3077 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 14.091s | Local Loss: 0.0586 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 12.683s | Local Loss: 0.0371 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 13.231s | Local Loss: 0.0532 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 13.278s | Local Loss: 0.0472 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 12.847s | Local Loss: 0.0183 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 14.199s | Local Loss: 0.0675 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 13.079s | Local Loss: 0.0535 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 13.576s | Local Loss: 0.4791 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 13.114s | Local Loss: 0.0980 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 12.773s | Local Loss: 0.0197 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 13.672s | Local Loss: 0.2590 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 13.949s | Local Loss: 0.3106 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 13.184s | Local Loss: 0.0406 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 14.248s | Local Loss: 0.3831 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 13.437s | Local Loss: 0.0444 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 14.079s | Local Loss: 0.0466 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 13.838s | Local Loss: 0.0603 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 14.076s | Local Loss: 0.1312 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 12.933s | Local Loss: 0.0482 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 13.336s | Local Loss: 0.1173 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 13.198s | Local Loss: 0.2365 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 13.551s | Local Loss: 0.3394 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 13.049s | Local Loss: 0.0226 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 13.556s | Local Loss: 0.0998 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 13.665s | Local Loss: 0.4055 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 13.194s | Local Loss: 0.0269 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 21.288s | Local Loss: 0.0423 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 16.051s | Local Loss: 0.0407 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 17.009s | Local Loss: 0.0408 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 17.718s | Local Loss: 0.0605 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 17.683s | Local Loss: 0.1896 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 16.732s | Local Loss: 0.0088 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 16.802s | Local Loss: 0.0252 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 17.957s | Local Loss: 0.3382 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 17.277s | Local Loss: 0.0185 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 16.646s | Local Loss: 0.0403 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 16.192s | Local Loss: 0.0519 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 16.261s | Local Loss: 0.4982 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 17.098s | Local Loss: 0.0706 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 16.998s | Local Loss: 0.0227 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 16.597s | Local Loss: 0.1400 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 16.234s | Local Loss: 0.4146 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 16.647s | Local Loss: 0.1538 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 17.817s | Local Loss: 0.0357 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 13.962s | Local Loss: 0.1043 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 13.349s | Local Loss: 0.0620 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 14.246s | Local Loss: 0.0702 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 13.784s | Local Loss: 0.0398 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 13.013s | Local Loss: 0.1634 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 13.764s | Local Loss: 0.0803 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 13.812s | Local Loss: 0.0428 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 13.087s | Local Loss: 0.0386 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 13.234s | Local Loss: 0.3491 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 13.220s | Local Loss: 0.0776 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 14.618s | Local Loss: 0.0662 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 15.512s | Local Loss: 0.2508 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 13.822s | Local Loss: 0.0297 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 14.051s | Local Loss: 0.0791 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 13.849s | Local Loss: 0.0822 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 13.691s | Local Loss: 0.3123 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 13.696s | Local Loss: 0.3632 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 13.839s | Local Loss: 0.1623 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 13.507s | Local Loss: 0.0196 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 13.553s | Local Loss: 0.0741 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 13.788s | Local Loss: 0.0918 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 13.249s | Local Loss: 0.0134 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 13.212s | Local Loss: 0.0596 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 13.366s | Local Loss: 0.0192 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 13.577s | Local Loss: 0.0207 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 16.253s | Local Loss: 0.4195 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 17.067s | Local Loss: 0.1112 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 15.734s | Local Loss: 0.2637 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 15.397s | Local Loss: 0.0289 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 15.787s | Local Loss: 0.3732 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 13.598s | Local Loss: 0.0465 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 14.184s | Local Loss: 0.0655 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 21.729s | Local Loss: 0.0390 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 16.990s | Local Loss: 0.0000 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 16.145s | Local Loss: 0.0967 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 17.798s | Local Loss: 0.0582 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 13.736s | Local Loss: 0.0309 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 13.639s | Local Loss: 0.0187 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 12.958s | Local Loss: 0.0000 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 14.352s | Local Loss: 0.0287 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 13.312s | Local Loss: 0.0386 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 12.945s | Local Loss: 0.0242 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 13.294s | Local Loss: 0.1382 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 22.171s | Local Loss: 0.0634 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 20.548s | Local Loss: 0.0257 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 14.067s | Local Loss: 0.0514 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 13.709s | Local Loss: 0.0466 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 16.673s | Local Loss: 0.0231 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 14.054s | Local Loss: 0.0895 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 15.099s | Local Loss: 0.0514 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 13.547s | Local Loss: 0.1094 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 15.119s | Local Loss: 0.0838 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 13.376s | Local Loss: 0.0394 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 13.832s | Local Loss: 0.2197 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 13.680s | Local Loss: 0.0170 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 13.825s | Local Loss: 0.0249 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 14.562s | Local Loss: 0.0506 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 13.800s | Local Loss: 0.0262 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 13.459s | Local Loss: 0.0181 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 13.517s | Local Loss: 0.1439 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 14.202s | Local Loss: 0.0239 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 14.379s | Local Loss: 0.0224 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 13.815s | Local Loss: 0.0448 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 14.296s | Local Loss: 0.0000 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 13.422s | Local Loss: 0.4534 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 13.948s | Local Loss: 0.0265 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 13.414s | Local Loss: 0.0449 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 13.690s | Local Loss: 0.0042 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 14.305s | Local Loss: 0.0295 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 13.294s | Local Loss: 0.1964 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 14.371s | Local Loss: 0.0388 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 13.863s | Local Loss: 0.0195 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 13.890s | Local Loss: 0.1894 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 14.372s | Local Loss: 0.0516 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 13.297s | Local Loss: 0.0387 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 13.858s | Local Loss: 0.0642 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 14.333s | Local Loss: 0.0398 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 13.839s | Local Loss: 0.0252 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 14.647s | Local Loss: 0.3212 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 13.178s | Local Loss: 0.1174 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 14.612s | Local Loss: 0.0134 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 13.755s | Local Loss: 0.0292 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 21.272s | Local Loss: 0.0192 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 15.610s | Local Loss: 0.0204 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 14.635s | Local Loss: 0.0572 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 16.756s | Local Loss: 0.0601 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 14.295s | Local Loss: 0.0861 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 14.991s | Local Loss: 0.1082 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 14.429s | Local Loss: 0.0511 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 15.598s | Local Loss: 0.0000 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 15.835s | Local Loss: 0.0412 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 13.104s | Local Loss: 0.0665 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 14.560s | Local Loss: 0.0393 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 13.135s | Local Loss: 0.0267 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 13.044s | Local Loss: 0.0198 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 14.599s | Local Loss: 0.0043 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 12.966s | Local Loss: 0.0388 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 14.402s | Local Loss: 0.2294 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 13.389s | Local Loss: 0.0119 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 13.044s | Local Loss: 0.0734 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 14.420s | Local Loss: 0.0126 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 13.106s | Local Loss: 0.0228 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 13.115s | Local Loss: 0.0346 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 13.980s | Local Loss: 0.0454 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 13.305s | Local Loss: 0.0945 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 13.790s | Local Loss: 0.0228 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 13.229s | Local Loss: 0.0259 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 13.341s | Local Loss: 0.0494 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 14.415s | Local Loss: 0.0318 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 13.316s | Local Loss: 0.1524 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 14.620s | Local Loss: 0.0508 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 13.882s | Local Loss: 0.0105 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 13.176s | Local Loss: 0.1231 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 14.694s | Local Loss: 0.0100 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 13.247s | Local Loss: 0.0105 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 13.504s | Local Loss: 0.0282 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 15.380s | Local Loss: 0.0215 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 12.862s | Local Loss: 0.0248 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 13.778s | Local Loss: 0.0789 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 13.889s | Local Loss: 0.2470 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 13.632s | Local Loss: 0.0349 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 13.558s | Local Loss: 0.0129 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 13.165s | Local Loss: 0.0287 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 13.948s | Local Loss: 0.2297 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 14.271s | Local Loss: 0.0183 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 13.588s | Local Loss: 0.0289 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 13.429s | Local Loss: 0.4254 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 13.141s | Local Loss: 0.0410 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 13.204s | Local Loss: 0.0404 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 13.932s | Local Loss: 0.0151 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 13.867s | Local Loss: 0.1410 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 13.806s | Local Loss: 0.0364 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 24.060s | Local Loss: 0.0659 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 18.446s | Local Loss: 0.0908 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 16.960s | Local Loss: 0.1524 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 18.827s | Local Loss: 0.0076 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 18.574s | Local Loss: 0.0194 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 20.060s | Local Loss: 0.0896 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 17.101s | Local Loss: 0.0228 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 18.519s | Local Loss: 0.0184 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 17.315s | Local Loss: 0.0244 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 18.252s | Local Loss: 0.1191 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 17.330s | Local Loss: 0.0689 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 16.905s | Local Loss: 0.0112 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 17.260s | Local Loss: 0.1499 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 16.804s | Local Loss: 0.0335 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 18.261s | Local Loss: 0.0092 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 13.172s | Local Loss: 0.0318 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 13.683s | Local Loss: 0.0239 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 13.846s | Local Loss: 0.0077 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 14.076s | Local Loss: 0.0157 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 13.946s | Local Loss: 0.3090 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 13.179s | Local Loss: 0.0870 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 13.522s | Local Loss: 0.0813 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 14.067s | Local Loss: 0.0107 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 16.430s | Local Loss: 0.0276 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 15.560s | Local Loss: 0.1301 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 15.163s | Local Loss: 0.0190 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 16.731s | Local Loss: 0.1679 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 14.443s | Local Loss: 0.0173 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 13.884s | Local Loss: 0.3513 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 14.612s | Local Loss: 0.2707 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 14.769s | Local Loss: 0.0221 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 14.743s | Local Loss: 0.0338 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 14.508s | Local Loss: 0.0281 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 13.401s | Local Loss: 0.0999 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 14.899s | Local Loss: 0.0155 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 13.494s | Local Loss: 0.0699 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 13.893s | Local Loss: 0.0558 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 14.545s | Local Loss: 0.0601 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 14.169s | Local Loss: 0.0291 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 16.167s | Local Loss: 0.0573 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 15.567s | Local Loss: 0.0260 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 15.034s | Local Loss: 0.0340 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 14.369s | Local Loss: 0.0936 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 14.182s | Local Loss: 0.1929 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 14.017s | Local Loss: 0.0261 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 13.385s | Local Loss: 0.0000 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 13.849s | Local Loss: 0.4091 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 13.616s | Local Loss: 0.0337 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 14.102s | Local Loss: 0.0200 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 13.617s | Local Loss: 0.1462 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 18.798s | Local Loss: 0.0160 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 16.505s | Local Loss: 0.0969 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 17.082s | Local Loss: 0.0710 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 16.380s | Local Loss: 0.0321 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 16.817s | Local Loss: 0.0258 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 16.501s | Local Loss: 0.1643 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 15.137s | Local Loss: 0.0219 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 16.385s | Local Loss: 0.2924 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 14.440s | Local Loss: 0.0473 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 19.176s | Local Loss: 0.1097 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 24.233s | Local Loss: 0.0123 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 16.883s | Local Loss: 0.0133 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 15.086s | Local Loss: 0.0863 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 14.895s | Local Loss: 0.0107 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 15.197s | Local Loss: 0.0174 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 14.904s | Local Loss: 0.0148 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 15.567s | Local Loss: 0.1476 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 15.324s | Local Loss: 0.0144 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 15.253s | Local Loss: 0.0261 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 13.539s | Local Loss: 0.2143 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 13.240s | Local Loss: 0.0445 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 12.952s | Local Loss: 0.1846 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 13.741s | Local Loss: 0.0608 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 14.262s | Local Loss: 0.0310 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 14.524s | Local Loss: 0.1432 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 14.187s | Local Loss: 0.0881 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 13.717s | Local Loss: 0.0209 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 13.467s | Local Loss: 0.1918 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 13.128s | Local Loss: 0.0126 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 14.792s | Local Loss: 0.0076 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 13.038s | Local Loss: 0.0101 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 13.758s | Local Loss: 0.0695 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 13.386s | Local Loss: 0.0429 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 13.204s | Local Loss: 0.0078 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 14.470s | Local Loss: 0.0205 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 13.239s | Local Loss: 0.0207 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 12.905s | Local Loss: 0.1096 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 14.483s | Local Loss: 0.0290 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 13.171s | Local Loss: 0.1112 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 13.415s | Local Loss: 0.0113 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 13.683s | Local Loss: 0.2660 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 13.351s | Local Loss: 0.0144 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 14.035s | Local Loss: 0.0114 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 13.222s | Local Loss: 0.0113 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 13.206s | Local Loss: 0.0259 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 13.813s | Local Loss: 0.0116 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 12.985s | Local Loss: 0.0375 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 14.264s | Local Loss: 0.0183 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 12.992s | Local Loss: 0.0413 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 13.202s | Local Loss: 0.0154 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 21.525s | Local Loss: 0.0170 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 17.316s | Local Loss: 0.0216 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 16.720s | Local Loss: 0.0243 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 17.290s | Local Loss: 0.0109 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 16.687s | Local Loss: 0.0443 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 17.671s | Local Loss: 0.0161 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 18.181s | Local Loss: 0.1164 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 14.478s | Local Loss: 0.0123 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 12.950s | Local Loss: 0.0150 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 14.234s | Local Loss: 0.0568 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 13.479s | Local Loss: 0.2924 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 13.421s | Local Loss: 0.0106 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 13.280s | Local Loss: 0.0824 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 13.949s | Local Loss: 0.0180 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 13.952s | Local Loss: 0.0225 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 13.231s | Local Loss: 0.0334 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 14.131s | Local Loss: 0.0237 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 13.603s | Local Loss: 0.2798 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 13.071s | Local Loss: 0.0305 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 14.210s | Local Loss: 0.0150 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 13.944s | Local Loss: 0.0080 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 13.281s | Local Loss: 0.0189 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 13.229s | Local Loss: 0.0123 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 12.982s | Local Loss: 0.0665 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 13.335s | Local Loss: 0.0104 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 13.653s | Local Loss: 0.0099 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 12.929s | Local Loss: 0.0193 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 13.245s | Local Loss: 0.0130 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 13.499s | Local Loss: 0.0078 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 13.836s | Local Loss: 0.0141 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 13.577s | Local Loss: 0.0178 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 13.775s | Local Loss: 0.0087 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 14.118s | Local Loss: 0.0123 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 13.500s | Local Loss: 0.0184 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 14.622s | Local Loss: 0.0227 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 13.020s | Local Loss: 0.0124 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 13.490s | Local Loss: 0.0175 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 13.489s | Local Loss: 0.0167 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 13.357s | Local Loss: 0.1423 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 13.710s | Local Loss: 0.0408 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 12.941s | Local Loss: 0.0444 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 13.883s | Local Loss: 0.0130 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 13.933s | Local Loss: 0.0317 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 13.119s | Local Loss: 0.0130 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 13.901s | Local Loss: 0.0102 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 13.272s | Local Loss: 0.0289 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 13.935s | Local Loss: 0.0120 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 13.428s | Local Loss: 0.0493 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 13.619s | Local Loss: 0.0168 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 14.455s | Local Loss: 0.0144 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 21.108s | Local Loss: 0.1895 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 17.204s | Local Loss: 0.0042 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 16.182s | Local Loss: 0.0194 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 16.844s | Local Loss: 0.0365 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 17.061s | Local Loss: 0.1403 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 16.709s | Local Loss: 0.0000 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 18.575s | Local Loss: 0.1417 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 18.383s | Local Loss: 0.0055 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 17.881s | Local Loss: 0.0066 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 17.148s | Local Loss: 0.0389 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 17.701s | Local Loss: 0.0068 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 18.163s | Local Loss: 0.0445 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 14.213s | Local Loss: 0.0320 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 13.454s | Local Loss: 0.2349 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 13.615s | Local Loss: 0.0100 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 14.019s | Local Loss: 0.0196 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 13.204s | Local Loss: 0.0975 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 13.415s | Local Loss: 0.0174 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 15.498s | Local Loss: 0.0097 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 12.981s | Local Loss: 0.0169 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 13.873s | Local Loss: 0.0254 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 13.982s | Local Loss: 0.0079 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 13.310s | Local Loss: 0.0572 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 14.172s | Local Loss: 0.0157 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 12.957s | Local Loss: 0.0066 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 13.788s | Local Loss: 0.0061 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 13.644s | Local Loss: 0.0152 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 14.479s | Local Loss: 0.0441 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 13.143s | Local Loss: 0.0081 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 14.238s | Local Loss: 0.0148 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 14.779s | Local Loss: 0.0105 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 13.876s | Local Loss: 0.0106 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 14.169s | Local Loss: 0.0068 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 14.397s | Local Loss: 0.0074 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 14.246s | Local Loss: 0.0145 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 13.503s | Local Loss: 0.1738 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 14.530s | Local Loss: 0.0188 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 14.378s | Local Loss: 0.2770 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 13.543s | Local Loss: 0.2494 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 14.064s | Local Loss: 0.0092 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 14.179s | Local Loss: 0.0074 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 13.226s | Local Loss: 0.0098 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 13.154s | Local Loss: 0.0191 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 15.501s | Local Loss: 0.0077 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 12.992s | Local Loss: 0.0072 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 13.575s | Local Loss: 0.0099 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 13.728s | Local Loss: 0.0190 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 13.482s | Local Loss: 0.0512 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 14.229s | Local Loss: 0.0165 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 13.336s | Local Loss: 0.0137 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 22:41:07] Logger closing.
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 16.401s | Local Loss: 0.1878 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 14.164s | Local Loss: 0.0850 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.435s | Local Loss: 0.5210 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.140s | Local Loss: 0.4083 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 12.946s | Local Loss: 0.0467 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 12.114s | Local Loss: 0.1065 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.339s | Local Loss: 0.3312 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 12.447s | Local Loss: 0.3000 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.841s | Local Loss: 0.1700 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 15.124s | Local Loss: 0.1504 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 14.159s | Local Loss: 0.1628 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.091s | Local Loss: 0.0713 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 13.563s | Local Loss: 0.2277 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.139s | Local Loss: 0.3900 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 12.950s | Local Loss: 0.4415 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.419s | Local Loss: 0.0267 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 11.997s | Local Loss: 0.0439 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 12.024s | Local Loss: 0.2185 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 11.862s | Local Loss: 0.2022 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 11.834s | Local Loss: 0.0902 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 12.843s | Local Loss: 0.0856 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 11.677s | Local Loss: 0.0680 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 12.979s | Local Loss: 0.0101 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 11.973s | Local Loss: 0.0374 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.231s | Local Loss: 0.1048 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.564s | Local Loss: 0.1046 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 11.975s | Local Loss: 0.2650 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 11.720s | Local Loss: 0.0963 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.401s | Local Loss: 0.2197 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.396s | Local Loss: 0.4122 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.519s | Local Loss: 0.0783 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 13.236s | Local Loss: 0.0702 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 11.615s | Local Loss: 0.0502 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 11.680s | Local Loss: 0.2278 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.401s | Local Loss: 0.1444 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.276s | Local Loss: 0.0382 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 11.692s | Local Loss: 0.0156 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.326s | Local Loss: 0.3768 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 11.882s | Local Loss: 0.1443 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 11.711s | Local Loss: 0.3722 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 12.126s | Local Loss: 0.2173 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.554s | Local Loss: 0.1184 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 11.945s | Local Loss: 0.0872 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 12.911s | Local Loss: 0.0806 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 11.936s | Local Loss: 0.5683 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 11.849s | Local Loss: 0.1608 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 11.964s | Local Loss: 0.1212 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 11.934s | Local Loss: 0.1640 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 11.805s | Local Loss: 0.0589 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 12.016s | Local Loss: 0.1357 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 21.071s | Local Loss: 0.0071 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 15.514s | Local Loss: 0.0591 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 15.806s | Local Loss: 0.0243 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 13.965s | Local Loss: 0.0162 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 13.470s | Local Loss: 0.0750 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 13.795s | Local Loss: 0.0743 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 14.927s | Local Loss: 0.1156 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 13.306s | Local Loss: 0.0309 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 13.255s | Local Loss: 0.3277 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 13.566s | Local Loss: 0.2615 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 13.366s | Local Loss: 0.1297 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 13.016s | Local Loss: 0.0727 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 13.544s | Local Loss: 0.0540 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 13.096s | Local Loss: 0.4427 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 13.011s | Local Loss: 0.1261 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 13.246s | Local Loss: 0.3721 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 13.017s | Local Loss: 0.3414 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 13.302s | Local Loss: 0.1417 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 12.886s | Local Loss: 0.1540 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 13.050s | Local Loss: 0.3605 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 12.890s | Local Loss: 0.0968 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 13.121s | Local Loss: 0.0791 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 13.337s | Local Loss: 0.0287 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 13.303s | Local Loss: 0.3622 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 13.123s | Local Loss: 0.0581 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 13.331s | Local Loss: 0.0490 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 12.994s | Local Loss: 0.0934 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 13.101s | Local Loss: 0.0499 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 13.413s | Local Loss: 0.2188 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 13.126s | Local Loss: 0.1238 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 12.762s | Local Loss: 0.1382 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 13.019s | Local Loss: 0.2328 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 12.786s | Local Loss: 0.2101 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 13.340s | Local Loss: 0.0368 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 12.858s | Local Loss: 0.0687 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 12.874s | Local Loss: 0.0149 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 13.363s | Local Loss: 0.1415 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 13.040s | Local Loss: 0.0987 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 13.009s | Local Loss: 0.1218 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 12.928s | Local Loss: 0.1469 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 12.565s | Local Loss: 0.0738 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 13.592s | Local Loss: 0.0826 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 13.019s | Local Loss: 0.1060 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 13.122s | Local Loss: 0.0623 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 13.200s | Local Loss: 0.0542 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 12.956s | Local Loss: 0.0291 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 13.269s | Local Loss: 0.1102 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 12.724s | Local Loss: 0.0297 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 12.828s | Local Loss: 0.0234 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 13.175s | Local Loss: 0.1278 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 20.051s | Local Loss: 0.0773 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 17.121s | Local Loss: 0.3017 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 18.027s | Local Loss: 0.0296 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 16.456s | Local Loss: 0.0651 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 17.198s | Local Loss: 0.5399 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 17.610s | Local Loss: 0.1747 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 17.401s | Local Loss: 0.0687 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 18.451s | Local Loss: 0.2668 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 13.646s | Local Loss: 0.0431 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 14.303s | Local Loss: 0.1899 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 13.561s | Local Loss: 0.0364 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 13.800s | Local Loss: 0.0701 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 13.588s | Local Loss: 0.1309 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 13.338s | Local Loss: 0.0716 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 13.598s | Local Loss: 0.0893 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 13.414s | Local Loss: 0.0965 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 13.604s | Local Loss: 0.1785 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 13.680s | Local Loss: 0.2845 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 14.351s | Local Loss: 0.1131 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 13.633s | Local Loss: 0.0300 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 13.799s | Local Loss: 0.0486 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 13.616s | Local Loss: 0.0821 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 13.368s | Local Loss: 0.0232 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 13.441s | Local Loss: 0.3107 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 13.470s | Local Loss: 0.0594 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 14.240s | Local Loss: 0.0372 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 13.678s | Local Loss: 0.0545 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 13.496s | Local Loss: 0.0472 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 13.978s | Local Loss: 0.0186 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 13.733s | Local Loss: 0.0682 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 13.377s | Local Loss: 0.0523 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 13.598s | Local Loss: 0.4803 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 13.748s | Local Loss: 0.0991 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 13.877s | Local Loss: 0.0203 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 13.653s | Local Loss: 0.2615 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 13.236s | Local Loss: 0.3127 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 13.773s | Local Loss: 0.0412 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 13.623s | Local Loss: 0.3833 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 13.607s | Local Loss: 0.0448 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 13.683s | Local Loss: 0.0470 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 13.966s | Local Loss: 0.0629 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 14.115s | Local Loss: 0.1324 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 13.860s | Local Loss: 0.0485 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 13.301s | Local Loss: 0.1191 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 14.029s | Local Loss: 0.2388 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 13.536s | Local Loss: 0.3422 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 13.497s | Local Loss: 0.0184 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 13.418s | Local Loss: 0.1009 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 13.641s | Local Loss: 0.4086 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 13.843s | Local Loss: 0.0257 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 19.839s | Local Loss: 0.0418 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 17.047s | Local Loss: 0.0407 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 17.890s | Local Loss: 0.0432 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 16.781s | Local Loss: 0.0610 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 17.694s | Local Loss: 0.1921 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 16.487s | Local Loss: 0.0079 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 17.236s | Local Loss: 0.0282 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 17.508s | Local Loss: 0.3408 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 17.938s | Local Loss: 0.0189 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 16.813s | Local Loss: 0.0412 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 19.173s | Local Loss: 0.0528 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 13.443s | Local Loss: 0.5003 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 14.203s | Local Loss: 0.0715 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 13.556s | Local Loss: 0.0225 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 13.783s | Local Loss: 0.1418 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 13.714s | Local Loss: 0.4167 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 13.103s | Local Loss: 0.1556 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 13.845s | Local Loss: 0.0364 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 13.428s | Local Loss: 0.1052 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 13.465s | Local Loss: 0.0630 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 13.576s | Local Loss: 0.0719 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 13.261s | Local Loss: 0.0398 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 13.619s | Local Loss: 0.1649 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 13.365s | Local Loss: 0.0809 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 13.510s | Local Loss: 0.0442 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 13.844s | Local Loss: 0.0364 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 13.570s | Local Loss: 0.3521 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 13.126s | Local Loss: 0.0779 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 13.740s | Local Loss: 0.0664 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 13.828s | Local Loss: 0.2538 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 13.712s | Local Loss: 0.0307 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 13.623s | Local Loss: 0.0794 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 13.984s | Local Loss: 0.0833 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 13.750s | Local Loss: 0.3159 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 13.850s | Local Loss: 0.3670 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 13.603s | Local Loss: 0.1645 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 13.668s | Local Loss: 0.0197 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 13.519s | Local Loss: 0.0745 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 13.497s | Local Loss: 0.0930 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 13.653s | Local Loss: 0.0168 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 13.663s | Local Loss: 0.0604 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 13.371s | Local Loss: 0.0189 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 13.751s | Local Loss: 0.0166 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 13.745s | Local Loss: 0.4219 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 13.649s | Local Loss: 0.1131 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 13.205s | Local Loss: 0.2650 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 13.366s | Local Loss: 0.0291 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 13.868s | Local Loss: 0.3717 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 14.033s | Local Loss: 0.0469 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 13.171s | Local Loss: 0.0650 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 18.154s | Local Loss: 0.0388 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 15.539s | Local Loss: 0.0000 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 15.233s | Local Loss: 0.0968 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 15.333s | Local Loss: 0.0581 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 15.430s | Local Loss: 0.0279 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 15.091s | Local Loss: 0.0178 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 15.480s | Local Loss: 0.0000 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 15.448s | Local Loss: 0.0306 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 14.779s | Local Loss: 0.0352 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 15.110s | Local Loss: 0.0243 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 15.404s | Local Loss: 0.1386 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 15.277s | Local Loss: 0.0646 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 14.966s | Local Loss: 0.0248 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 15.388s | Local Loss: 0.0520 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 15.376s | Local Loss: 0.0478 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 15.341s | Local Loss: 0.0217 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 14.732s | Local Loss: 0.0909 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 16.621s | Local Loss: 0.0521 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 13.638s | Local Loss: 0.1115 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 13.913s | Local Loss: 0.0847 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 13.581s | Local Loss: 0.0396 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 13.730s | Local Loss: 0.2210 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 13.349s | Local Loss: 0.0213 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 13.681s | Local Loss: 0.0247 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 13.332s | Local Loss: 0.0514 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 13.738s | Local Loss: 0.0250 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 13.720s | Local Loss: 0.0170 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 13.547s | Local Loss: 0.1463 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 13.428s | Local Loss: 0.0275 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 13.636s | Local Loss: 0.0237 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 13.234s | Local Loss: 0.0455 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 13.615s | Local Loss: 0.0000 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 13.817s | Local Loss: 0.4541 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 13.680s | Local Loss: 0.0215 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 13.507s | Local Loss: 0.0598 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 13.748s | Local Loss: 0.0039 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 13.561s | Local Loss: 0.0399 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 13.758s | Local Loss: 0.2008 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 13.791s | Local Loss: 0.0385 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 13.335s | Local Loss: 0.0211 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 13.467s | Local Loss: 0.1959 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 13.971s | Local Loss: 0.0573 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 13.643s | Local Loss: 0.0426 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 13.471s | Local Loss: 0.0663 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 13.994s | Local Loss: 0.0442 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 13.744s | Local Loss: 0.0286 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 13.541s | Local Loss: 0.3220 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 13.851s | Local Loss: 0.1203 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 13.618s | Local Loss: 0.0147 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 13.594s | Local Loss: 0.0310 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 20.149s | Local Loss: 0.0223 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 17.337s | Local Loss: 0.0226 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 17.828s | Local Loss: 0.0579 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 17.398s | Local Loss: 0.0613 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 17.461s | Local Loss: 0.0880 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 18.510s | Local Loss: 0.1102 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 14.463s | Local Loss: 0.0523 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 13.745s | Local Loss: 0.0000 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 13.517s | Local Loss: 0.0423 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 13.981s | Local Loss: 0.0679 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 13.457s | Local Loss: 0.0307 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 13.735s | Local Loss: 0.0310 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 13.595s | Local Loss: 0.0201 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 14.077s | Local Loss: 0.0058 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 13.643s | Local Loss: 0.0391 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 13.562s | Local Loss: 0.2338 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 13.242s | Local Loss: 0.0103 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 14.837s | Local Loss: 0.0749 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 13.201s | Local Loss: 0.0129 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 13.389s | Local Loss: 0.0290 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 13.736s | Local Loss: 0.0353 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 13.527s | Local Loss: 0.0457 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 21.389s | Local Loss: 0.0960 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 20.766s | Local Loss: 0.0230 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 22.743s | Local Loss: 0.0258 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 20.683s | Local Loss: 0.0501 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 20.595s | Local Loss: 0.0297 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 21.169s | Local Loss: 0.1545 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 25.067s | Local Loss: 0.0510 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 20.283s | Local Loss: 0.0114 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 21.113s | Local Loss: 0.1239 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 20.360s | Local Loss: 0.0094 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 21.594s | Local Loss: 0.0102 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 20.269s | Local Loss: 0.0283 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 21.116s | Local Loss: 0.0207 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 24.012s | Local Loss: 0.0235 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 21.763s | Local Loss: 0.0826 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 20.146s | Local Loss: 0.2516 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 22.427s | Local Loss: 0.0348 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 20.912s | Local Loss: 0.0105 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 25.215s | Local Loss: 0.0298 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 20.157s | Local Loss: 0.2317 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 21.485s | Local Loss: 0.0193 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 20.993s | Local Loss: 0.0331 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 21.602s | Local Loss: 0.4264 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 16.130s | Local Loss: 0.0411 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 13.232s | Local Loss: 0.0403 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 13.484s | Local Loss: 0.0162 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 13.633s | Local Loss: 0.1409 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 13.655s | Local Loss: 0.0370 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 18.296s | Local Loss: 0.0703 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 15.715s | Local Loss: 0.0924 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 15.639s | Local Loss: 0.1561 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 15.268s | Local Loss: 0.0067 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 15.018s | Local Loss: 0.0196 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 15.525s | Local Loss: 0.0903 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 15.282s | Local Loss: 0.0236 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 15.367s | Local Loss: 0.0184 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 15.078s | Local Loss: 0.0236 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 15.174s | Local Loss: 0.1223 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 15.947s | Local Loss: 0.0699 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 13.706s | Local Loss: 0.0114 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 13.488s | Local Loss: 0.1510 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 13.492s | Local Loss: 0.0317 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 13.237s | Local Loss: 0.0083 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 13.449s | Local Loss: 0.0314 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 14.171s | Local Loss: 0.0223 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 13.473s | Local Loss: 0.0086 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 13.470s | Local Loss: 0.0164 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 13.864s | Local Loss: 0.3094 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 13.631s | Local Loss: 0.0876 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 14.354s | Local Loss: 0.0807 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 14.682s | Local Loss: 0.0140 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 14.222s | Local Loss: 0.0276 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 14.365s | Local Loss: 0.1327 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 14.482s | Local Loss: 0.0179 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 14.429s | Local Loss: 0.1689 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 13.817s | Local Loss: 0.0171 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 13.993s | Local Loss: 0.3488 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 13.642s | Local Loss: 0.2663 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 14.055s | Local Loss: 0.0206 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 13.676s | Local Loss: 0.0320 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 14.010s | Local Loss: 0.0266 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 13.892s | Local Loss: 0.0997 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 13.691s | Local Loss: 0.0151 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 13.755s | Local Loss: 0.0684 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 14.226s | Local Loss: 0.0560 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 13.548s | Local Loss: 0.0603 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 13.536s | Local Loss: 0.0277 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 14.195s | Local Loss: 0.0568 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 13.359s | Local Loss: 0.0241 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 13.506s | Local Loss: 0.0317 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 13.466s | Local Loss: 0.0879 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 13.889s | Local Loss: 0.1906 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 14.039s | Local Loss: 0.0258 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 14.320s | Local Loss: 0.0000 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 13.844s | Local Loss: 0.4059 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 14.134s | Local Loss: 0.0322 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 13.618s | Local Loss: 0.0192 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 13.819s | Local Loss: 0.1453 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 20.233s | Local Loss: 0.0168 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 17.787s | Local Loss: 0.0971 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 16.842s | Local Loss: 0.0721 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 18.116s | Local Loss: 0.0320 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 17.178s | Local Loss: 0.0256 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 19.300s | Local Loss: 0.1636 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 17.430s | Local Loss: 0.0230 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 18.275s | Local Loss: 0.2925 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 17.436s | Local Loss: 0.0488 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 17.719s | Local Loss: 0.1112 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 18.236s | Local Loss: 0.0166 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 18.443s | Local Loss: 0.0139 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 17.095s | Local Loss: 0.0884 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 25.224s | Local Loss: 0.0112 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 20.386s | Local Loss: 0.0191 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 18.881s | Local Loss: 0.0171 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 14.436s | Local Loss: 0.1517 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 13.662s | Local Loss: 0.0170 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 13.004s | Local Loss: 0.0269 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 13.307s | Local Loss: 0.2160 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 13.362s | Local Loss: 0.0456 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 12.802s | Local Loss: 0.1860 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 13.233s | Local Loss: 0.0622 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 13.054s | Local Loss: 0.0321 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 13.103s | Local Loss: 0.1444 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 13.351s | Local Loss: 0.0905 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 12.999s | Local Loss: 0.0211 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 13.444s | Local Loss: 0.1991 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 13.451s | Local Loss: 0.0142 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 12.839s | Local Loss: 0.0088 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 13.302s | Local Loss: 0.0123 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 13.145s | Local Loss: 0.0715 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 13.300s | Local Loss: 0.0455 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 12.909s | Local Loss: 0.0092 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 13.042s | Local Loss: 0.0213 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 13.313s | Local Loss: 0.0214 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 12.712s | Local Loss: 0.1122 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 13.180s | Local Loss: 0.0292 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 12.971s | Local Loss: 0.1142 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 13.194s | Local Loss: 0.0115 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 13.153s | Local Loss: 0.2666 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 13.935s | Local Loss: 0.0150 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 16.230s | Local Loss: 0.0115 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 14.248s | Local Loss: 0.0097 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 14.154s | Local Loss: 0.0261 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 13.361s | Local Loss: 0.0119 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 14.290s | Local Loss: 0.0391 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_3.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_3.log
new file mode 100644
index 0000000000000000000000000000000000000000..d34b586cad32cabc576b535f6ae563c8ee86481e
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_3.log
@@ -0,0 +1,900 @@
+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 17.775s | Local Loss: 0.5174 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 14.474s | Local Loss: 0.4169 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 13.692s | Local Loss: 0.3956 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 12.942s | Local Loss: 0.2516 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 12.197s | Local Loss: 0.2178 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 14.515s | Local Loss: 0.3145 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 23.098s | Local Loss: 0.2315 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 13.927s | Local Loss: 0.1238 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 14.156s | Local Loss: 0.1739 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 13.110s | Local Loss: 0.3872 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 13.826s | Local Loss: 0.0188 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 14.120s | Local Loss: 0.1395 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 14.900s | Local Loss: 0.1434 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 15.021s | Local Loss: 0.1473 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 13.286s | Local Loss: 0.1662 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 12.709s | Local Loss: 0.4028 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 13.661s | Local Loss: 0.2105 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 14.416s | Local Loss: 0.2584 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 13.503s | Local Loss: 0.1040 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 14.022s | Local Loss: 0.1300 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 12.583s | Local Loss: 0.0151 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 13.502s | Local Loss: 0.0000 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 13.397s | Local Loss: 0.1651 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 11.991s | Local Loss: 0.2491 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 12.595s | Local Loss: 0.4610 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 12.276s | Local Loss: 0.1755 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 12.020s | Local Loss: 0.1170 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 13.572s | Local Loss: 0.1239 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 12.141s | Local Loss: 0.2257 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 13.299s | Local Loss: 0.3813 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 13.054s | Local Loss: 0.0351 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 12.141s | Local Loss: 0.0314 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 12.789s | Local Loss: 0.3542 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 13.855s | Local Loss: 0.0689 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 12.557s | Local Loss: 0.0480 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 12.681s | Local Loss: 0.0140 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 13.729s | Local Loss: 0.4869 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 12.215s | Local Loss: 0.3869 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 12.238s | Local Loss: 0.5512 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 12.965s | Local Loss: 0.0253 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 13.270s | Local Loss: 0.0579 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 13.197s | Local Loss: 0.3149 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 13.415s | Local Loss: 0.2525 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 12.977s | Local Loss: 0.1701 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 12.561s | Local Loss: 0.0727 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 12.651s | Local Loss: 0.0315 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 12.691s | Local Loss: 0.0396 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 12.483s | Local Loss: 0.3525 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 13.067s | Local Loss: 0.2106 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 12.720s | Local Loss: 0.1306 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 20.925s | Local Loss: 0.0648 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 17.458s | Local Loss: 0.0612 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 16.931s | Local Loss: 0.0337 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 17.308s | Local Loss: 0.1757 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 14.148s | Local Loss: 0.0462 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 13.562s | Local Loss: 0.1301 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 13.497s | Local Loss: 0.0701 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 14.000s | Local Loss: 0.1641 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 12.953s | Local Loss: 0.3122 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 13.184s | Local Loss: 0.0230 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 14.095s | Local Loss: 0.0719 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 15.036s | Local Loss: 0.0334 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 14.480s | Local Loss: 0.0318 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 13.738s | Local Loss: 0.2454 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 15.075s | Local Loss: 0.4730 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 14.513s | Local Loss: 0.1191 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 15.539s | Local Loss: 0.0198 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 13.543s | Local Loss: 0.0000 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 14.119s | Local Loss: 0.0410 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 14.148s | Local Loss: 0.1355 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 13.657s | Local Loss: 0.1519 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 13.484s | Local Loss: 0.0401 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 13.540s | Local Loss: 0.1536 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 14.076s | Local Loss: 0.0272 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 13.622s | Local Loss: 0.1226 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 14.730s | Local Loss: 0.3838 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 13.500s | Local Loss: 0.0623 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 13.804s | Local Loss: 0.0347 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 13.774s | Local Loss: 0.0742 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 13.268s | Local Loss: 0.1394 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 13.511s | Local Loss: 0.0430 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 13.690s | Local Loss: 0.2251 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 12.985s | Local Loss: 0.0519 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 14.119s | Local Loss: 0.5502 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 13.687s | Local Loss: 0.1513 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 12.941s | Local Loss: 0.0709 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 13.879s | Local Loss: 0.4590 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 13.374s | Local Loss: 0.1330 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 13.619s | Local Loss: 0.1390 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 13.268s | Local Loss: 0.0397 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 13.648s | Local Loss: 0.3404 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 13.477s | Local Loss: 0.1416 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 13.385s | Local Loss: 0.1350 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 13.910s | Local Loss: 0.0481 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 13.212s | Local Loss: 0.2022 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 13.329s | Local Loss: 0.0678 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 13.263s | Local Loss: 0.0235 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 13.982s | Local Loss: 0.0309 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 13.256s | Local Loss: 0.0261 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 13.585s | Local Loss: 0.0328 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 20.559s | Local Loss: 0.0306 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 17.118s | Local Loss: 0.5220 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 14.804s | Local Loss: 0.0158 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 14.729s | Local Loss: 0.2146 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 15.082s | Local Loss: 0.2883 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 16.240s | Local Loss: 0.0055 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 16.018s | Local Loss: 0.3740 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 14.769s | Local Loss: 0.0402 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 14.819s | Local Loss: 0.1278 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 16.162s | Local Loss: 0.0249 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 14.988s | Local Loss: 0.0573 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 16.013s | Local Loss: 0.2661 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 14.202s | Local Loss: 0.1365 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 13.499s | Local Loss: 0.0572 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 13.808s | Local Loss: 0.1811 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 13.639s | Local Loss: 0.0593 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 13.954s | Local Loss: 0.0918 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 13.729s | Local Loss: 0.3023 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 13.706s | Local Loss: 0.0748 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 13.666s | Local Loss: 0.0219 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 13.307s | Local Loss: 0.1525 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 13.803s | Local Loss: 0.3521 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 13.568s | Local Loss: 0.0537 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 13.693s | Local Loss: 0.0767 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 14.538s | Local Loss: 0.1421 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 13.219s | Local Loss: 0.0501 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 13.720s | Local Loss: 0.0579 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 13.724s | Local Loss: 0.3246 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 13.187s | Local Loss: 0.0603 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 14.679s | Local Loss: 0.0375 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 13.486s | Local Loss: 0.0586 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 13.979s | Local Loss: 0.0365 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 13.407s | Local Loss: 0.2833 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 13.396s | Local Loss: 0.1472 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 14.144s | Local Loss: 0.0905 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 14.227s | Local Loss: 0.0492 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 13.667s | Local Loss: 0.0341 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 14.613s | Local Loss: 0.3951 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 13.843s | Local Loss: 0.0131 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 14.524s | Local Loss: 0.0326 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 14.368s | Local Loss: 0.1159 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 14.689s | Local Loss: 0.1584 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 13.380s | Local Loss: 0.1077 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 13.828s | Local Loss: 0.0631 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 13.705s | Local Loss: 0.0552 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 13.893s | Local Loss: 0.0475 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 13.514s | Local Loss: 0.0102 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 13.938s | Local Loss: 0.0469 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 14.097s | Local Loss: 0.0234 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 13.677s | Local Loss: 0.0571 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 21.970s | Local Loss: 0.1036 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 16.850s | Local Loss: 0.4231 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 17.570s | Local Loss: 0.0516 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 18.029s | Local Loss: 0.0454 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 18.232s | Local Loss: 0.0136 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 17.183s | Local Loss: 0.4127 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 17.248s | Local Loss: 0.0772 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 18.225s | Local Loss: 0.1097 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 17.728s | Local Loss: 0.2235 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 17.092s | Local Loss: 0.1722 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 16.756s | Local Loss: 0.1486 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 16.592s | Local Loss: 0.0586 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 17.573s | Local Loss: 0.0557 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 17.463s | Local Loss: 0.3737 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 17.140s | Local Loss: 0.0796 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 16.786s | Local Loss: 0.0247 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 17.183s | Local Loss: 0.0343 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 18.355s | Local Loss: 0.0156 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 14.625s | Local Loss: 0.0318 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 13.707s | Local Loss: 0.0345 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 14.673s | Local Loss: 0.1920 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 14.100s | Local Loss: 0.0270 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 13.264s | Local Loss: 0.0572 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 14.121s | Local Loss: 0.0461 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 14.163s | Local Loss: 0.0290 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 13.466s | Local Loss: 0.0740 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 13.637s | Local Loss: 0.0106 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 13.696s | Local Loss: 0.1248 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 15.161s | Local Loss: 0.4965 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 15.961s | Local Loss: 0.4462 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 14.041s | Local Loss: 0.0494 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 14.623s | Local Loss: 0.1353 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 14.135s | Local Loss: 0.3555 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 14.095s | Local Loss: 0.0272 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 14.108s | Local Loss: 0.3187 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 14.141s | Local Loss: 0.4212 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 13.952s | Local Loss: 0.1243 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 14.086s | Local Loss: 0.2320 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 14.075s | Local Loss: 0.0638 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 13.936s | Local Loss: 0.0475 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 13.682s | Local Loss: 0.0287 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 13.690s | Local Loss: 0.0269 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 14.098s | Local Loss: 0.0413 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 16.769s | Local Loss: 0.0732 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 17.281s | Local Loss: 0.0196 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 16.301s | Local Loss: 0.2173 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 15.685s | Local Loss: 0.1227 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 16.243s | Local Loss: 0.0136 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 14.062s | Local Loss: 0.0100 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 14.506s | Local Loss: 0.0281 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 22.358s | Local Loss: 0.0115 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 17.541s | Local Loss: 0.0823 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 16.726s | Local Loss: 0.1410 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 18.423s | Local Loss: 0.0445 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 14.363s | Local Loss: 0.0274 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 13.992s | Local Loss: 0.0333 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 13.556s | Local Loss: 0.0374 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 14.868s | Local Loss: 0.0963 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 13.755s | Local Loss: 0.2828 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 13.562s | Local Loss: 0.0081 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 13.857s | Local Loss: 0.1241 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 22.689s | Local Loss: 0.0195 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 21.079s | Local Loss: 0.0168 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 14.443s | Local Loss: 0.0264 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 14.161s | Local Loss: 0.1009 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 16.825s | Local Loss: 0.0252 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 14.359s | Local Loss: 0.0574 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 15.530s | Local Loss: 0.0359 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 14.021s | Local Loss: 0.1079 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 15.571s | Local Loss: 0.0245 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 13.852s | Local Loss: 0.1247 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 14.117s | Local Loss: 0.2882 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 14.147s | Local Loss: 0.0000 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 14.400s | Local Loss: 0.4373 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 15.096s | Local Loss: 0.0190 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 14.077s | Local Loss: 0.0657 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 13.845s | Local Loss: 0.0256 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 13.929s | Local Loss: 0.1049 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 14.589s | Local Loss: 0.0270 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 14.901s | Local Loss: 0.2180 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 13.562s | Local Loss: 0.0341 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 14.757s | Local Loss: 0.1103 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 13.639s | Local Loss: 0.0206 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 14.290s | Local Loss: 0.2500 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 13.860s | Local Loss: 0.0326 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 13.981s | Local Loss: 0.0223 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 14.753s | Local Loss: 0.0255 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 13.704s | Local Loss: 0.3542 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 14.725s | Local Loss: 0.2093 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 14.414s | Local Loss: 0.0208 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 14.157s | Local Loss: 0.0707 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 14.699s | Local Loss: 0.0163 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 13.657s | Local Loss: 0.0219 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 14.162s | Local Loss: 0.0548 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 14.796s | Local Loss: 0.2847 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 14.260s | Local Loss: 0.3681 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 14.965s | Local Loss: 0.0165 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 13.619s | Local Loss: 0.0408 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 15.049s | Local Loss: 0.0173 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 14.344s | Local Loss: 0.2533 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 21.863s | Local Loss: 0.1319 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 16.005s | Local Loss: 0.0263 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 15.023s | Local Loss: 0.1849 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 17.067s | Local Loss: 0.0901 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 14.641s | Local Loss: 0.0474 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 15.478s | Local Loss: 0.0904 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 14.884s | Local Loss: 0.0112 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 16.133s | Local Loss: 0.0278 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 16.233s | Local Loss: 0.1590 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 13.533s | Local Loss: 0.0656 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 14.990s | Local Loss: 0.3829 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 13.492s | Local Loss: 0.0504 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 13.492s | Local Loss: 0.0339 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 14.865s | Local Loss: 0.0299 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 13.289s | Local Loss: 0.0507 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 14.596s | Local Loss: 0.0134 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 13.748s | Local Loss: 0.2070 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 13.472s | Local Loss: 0.3218 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 14.960s | Local Loss: 0.0541 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 13.412s | Local Loss: 0.0366 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 13.488s | Local Loss: 0.0261 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 14.224s | Local Loss: 0.0501 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 13.721s | Local Loss: 0.0249 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 14.231s | Local Loss: 0.0611 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 13.546s | Local Loss: 0.0296 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 13.448s | Local Loss: 0.0276 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 14.744s | Local Loss: 0.0641 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 13.702s | Local Loss: 0.0433 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 14.861s | Local Loss: 0.0162 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 14.223s | Local Loss: 0.0310 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 13.590s | Local Loss: 0.0266 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 15.310s | Local Loss: 0.0213 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 13.494s | Local Loss: 0.0184 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 13.959s | Local Loss: 0.0275 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 15.691s | Local Loss: 0.0330 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 13.120s | Local Loss: 0.0256 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 14.110s | Local Loss: 0.0968 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 14.111s | Local Loss: 0.1319 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 14.125s | Local Loss: 0.0241 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 14.044s | Local Loss: 0.0297 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 13.538s | Local Loss: 0.0048 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 14.290s | Local Loss: 0.0128 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 14.617s | Local Loss: 0.0569 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 14.038s | Local Loss: 0.0192 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 13.797s | Local Loss: 0.0113 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 13.556s | Local Loss: 0.0105 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 13.710s | Local Loss: 0.0702 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 14.563s | Local Loss: 0.0164 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 14.231s | Local Loss: 0.0965 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 14.223s | Local Loss: 0.0179 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 24.489s | Local Loss: 0.0597 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 18.948s | Local Loss: 0.0112 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 17.249s | Local Loss: 0.4263 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 19.086s | Local Loss: 0.0571 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 18.815s | Local Loss: 0.1186 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 20.465s | Local Loss: 0.0249 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 17.555s | Local Loss: 0.0110 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 18.972s | Local Loss: 0.1794 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 17.432s | Local Loss: 0.0114 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 18.554s | Local Loss: 0.2617 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 17.603s | Local Loss: 0.0446 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 17.313s | Local Loss: 0.0217 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 17.624s | Local Loss: 0.0606 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 17.109s | Local Loss: 0.0279 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 18.559s | Local Loss: 0.0359 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 13.573s | Local Loss: 0.0214 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 13.887s | Local Loss: 0.0204 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 14.338s | Local Loss: 0.1541 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 14.482s | Local Loss: 0.0306 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 14.146s | Local Loss: 0.0181 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 13.522s | Local Loss: 0.0150 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 13.716s | Local Loss: 0.0182 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 14.319s | Local Loss: 0.0124 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 16.912s | Local Loss: 0.0387 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 15.796s | Local Loss: 0.0151 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 15.474s | Local Loss: 0.0185 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 17.061s | Local Loss: 0.0154 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 14.912s | Local Loss: 0.0098 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 14.300s | Local Loss: 0.0440 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 14.811s | Local Loss: 0.0700 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 15.209s | Local Loss: 0.0133 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 15.180s | Local Loss: 0.0164 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 14.780s | Local Loss: 0.0219 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 13.762s | Local Loss: 0.0202 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 15.196s | Local Loss: 0.0193 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 14.025s | Local Loss: 0.0128 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 14.285s | Local Loss: 0.1010 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 14.807s | Local Loss: 0.0201 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 14.543s | Local Loss: 0.0187 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 16.512s | Local Loss: 0.0181 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 15.895s | Local Loss: 0.2771 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 15.473s | Local Loss: 0.0607 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 14.729s | Local Loss: 0.0163 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 14.608s | Local Loss: 0.0229 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 14.259s | Local Loss: 0.0267 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 13.756s | Local Loss: 0.0681 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 14.176s | Local Loss: 0.0199 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 13.927s | Local Loss: 0.0275 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 14.558s | Local Loss: 0.0320 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 13.877s | Local Loss: 0.0203 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 19.296s | Local Loss: 0.0258 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 16.854s | Local Loss: 0.0236 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 17.707s | Local Loss: 0.3112 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 16.910s | Local Loss: 0.0214 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 17.396s | Local Loss: 0.0301 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 16.710s | Local Loss: 0.0108 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 15.223s | Local Loss: 0.0658 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 16.812s | Local Loss: 0.0303 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 14.734s | Local Loss: 0.0477 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 19.533s | Local Loss: 0.0541 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 24.415s | Local Loss: 0.0325 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 17.084s | Local Loss: 0.0535 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 15.222s | Local Loss: 0.0284 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 15.134s | Local Loss: 0.0266 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 15.417s | Local Loss: 0.0180 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 15.083s | Local Loss: 0.0355 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 15.751s | Local Loss: 0.1315 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 15.616s | Local Loss: 0.0291 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 15.485s | Local Loss: 0.0000 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 13.897s | Local Loss: 0.2058 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 13.334s | Local Loss: 0.0421 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 13.116s | Local Loss: 0.0129 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 14.022s | Local Loss: 0.0124 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 14.406s | Local Loss: 0.0100 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 14.833s | Local Loss: 0.0350 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 14.348s | Local Loss: 0.1643 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 13.927s | Local Loss: 0.0488 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 13.685s | Local Loss: 0.0255 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 13.288s | Local Loss: 0.0143 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 15.147s | Local Loss: 0.0187 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 13.067s | Local Loss: 0.0070 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 13.994s | Local Loss: 0.0478 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 13.553s | Local Loss: 0.0240 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 13.480s | Local Loss: 0.0932 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 14.592s | Local Loss: 0.0756 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 13.370s | Local Loss: 0.0152 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 13.139s | Local Loss: 0.0040 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 14.704s | Local Loss: 0.0219 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 13.376s | Local Loss: 0.0136 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 13.677s | Local Loss: 0.0107 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 13.754s | Local Loss: 0.0363 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 13.510s | Local Loss: 0.2852 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 14.357s | Local Loss: 0.0182 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 13.337s | Local Loss: 0.0922 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 13.448s | Local Loss: 0.0269 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 14.071s | Local Loss: 0.0269 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 13.284s | Local Loss: 0.0070 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 14.468s | Local Loss: 0.0175 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 13.212s | Local Loss: 0.0916 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 13.538s | Local Loss: 0.0275 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 21.813s | Local Loss: 0.0491 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 17.665s | Local Loss: 0.0614 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 17.082s | Local Loss: 0.0227 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 17.567s | Local Loss: 0.1038 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 16.968s | Local Loss: 0.0073 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 17.871s | Local Loss: 0.0047 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 18.506s | Local Loss: 0.0994 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 14.758s | Local Loss: 0.0501 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 13.284s | Local Loss: 0.0197 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 14.477s | Local Loss: 0.0141 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 13.680s | Local Loss: 0.0339 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 13.544s | Local Loss: 0.0207 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 13.468s | Local Loss: 0.0112 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 14.267s | Local Loss: 0.2471 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 14.085s | Local Loss: 0.0214 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 13.503s | Local Loss: 0.0243 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 14.086s | Local Loss: 0.1183 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 13.653s | Local Loss: 0.0146 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 13.197s | Local Loss: 0.0118 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 14.405s | Local Loss: 0.0177 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 14.159s | Local Loss: 0.0110 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 13.531s | Local Loss: 0.2701 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 13.438s | Local Loss: 0.0091 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 13.146s | Local Loss: 0.0198 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 13.408s | Local Loss: 0.0105 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 13.746s | Local Loss: 0.0063 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 13.226s | Local Loss: 0.0338 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 13.421s | Local Loss: 0.0977 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 13.668s | Local Loss: 0.0210 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 14.082s | Local Loss: 0.0087 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 13.678s | Local Loss: 0.0407 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 13.884s | Local Loss: 0.0117 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 14.471s | Local Loss: 0.0098 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 13.704s | Local Loss: 0.0188 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 14.741s | Local Loss: 0.0155 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 13.394s | Local Loss: 0.0104 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 13.717s | Local Loss: 0.0104 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 13.719s | Local Loss: 0.0219 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 13.704s | Local Loss: 0.0122 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 13.946s | Local Loss: 0.0346 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 13.177s | Local Loss: 0.0086 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 14.130s | Local Loss: 0.2067 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 14.068s | Local Loss: 0.0643 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 13.473s | Local Loss: 0.0591 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 14.052s | Local Loss: 0.0514 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 13.566s | Local Loss: 0.2996 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 14.148s | Local Loss: 0.0127 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 13.707s | Local Loss: 0.0324 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 13.972s | Local Loss: 0.0146 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 14.680s | Local Loss: 0.0747 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 21.426s | Local Loss: 0.0121 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 17.610s | Local Loss: 0.0074 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 16.536s | Local Loss: 0.0189 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 17.289s | Local Loss: 0.0123 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 17.214s | Local Loss: 0.0074 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 17.078s | Local Loss: 0.0221 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 18.789s | Local Loss: 0.0106 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 18.791s | Local Loss: 0.0204 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 18.311s | Local Loss: 0.0132 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 17.579s | Local Loss: 0.1701 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 17.966s | Local Loss: 0.0242 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 18.574s | Local Loss: 0.0368 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 14.687s | Local Loss: 0.0693 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 13.852s | Local Loss: 0.0409 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 14.031s | Local Loss: 0.0130 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 14.332s | Local Loss: 0.0117 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 13.567s | Local Loss: 0.0122 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 13.827s | Local Loss: 0.0460 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 15.824s | Local Loss: 0.0053 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 13.425s | Local Loss: 0.0095 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 14.261s | Local Loss: 0.0222 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 14.138s | Local Loss: 0.0056 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 13.575s | Local Loss: 0.0062 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 14.545s | Local Loss: 0.0819 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 13.274s | Local Loss: 0.0000 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 14.046s | Local Loss: 0.0141 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 13.968s | Local Loss: 0.0877 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 14.868s | Local Loss: 0.0064 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 13.343s | Local Loss: 0.0323 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 14.469s | Local Loss: 0.0160 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 15.086s | Local Loss: 0.0098 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 14.282s | Local Loss: 0.0361 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 14.494s | Local Loss: 0.0664 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 14.633s | Local Loss: 0.0224 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 14.633s | Local Loss: 0.1575 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 13.988s | Local Loss: 0.0122 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 14.930s | Local Loss: 0.0088 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 14.872s | Local Loss: 0.0431 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 13.710s | Local Loss: 0.0255 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 14.529s | Local Loss: 0.0604 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 14.445s | Local Loss: 0.0854 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 13.761s | Local Loss: 0.0361 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 13.414s | Local Loss: 0.0507 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 15.900s | Local Loss: 0.0117 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 13.304s | Local Loss: 0.0085 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 13.881s | Local Loss: 0.0746 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 13.922s | Local Loss: 0.0295 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 13.699s | Local Loss: 0.0146 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 14.302s | Local Loss: 0.0277 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 13.639s | Local Loss: 0.0723 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 22:10:07] Logger closing.
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 16.071s | Local Loss: 0.5174 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 13.810s | Local Loss: 0.4169 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.378s | Local Loss: 0.3957 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.189s | Local Loss: 0.2516 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 12.803s | Local Loss: 0.2178 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 12.041s | Local Loss: 0.3143 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.300s | Local Loss: 0.2317 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 12.531s | Local Loss: 0.1241 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.810s | Local Loss: 0.1739 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 14.859s | Local Loss: 0.3872 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 13.985s | Local Loss: 0.0187 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.044s | Local Loss: 0.1396 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 13.594s | Local Loss: 0.1435 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.066s | Local Loss: 0.1472 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 12.850s | Local Loss: 0.1665 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.237s | Local Loss: 0.4034 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 11.913s | Local Loss: 0.2108 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 11.927s | Local Loss: 0.2586 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 11.968s | Local Loss: 0.1087 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 11.837s | Local Loss: 0.1302 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 12.864s | Local Loss: 0.0149 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 11.634s | Local Loss: 0.0000 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 12.958s | Local Loss: 0.1652 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 11.737s | Local Loss: 0.2497 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.194s | Local Loss: 0.4615 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.413s | Local Loss: 0.1757 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 11.743s | Local Loss: 0.1197 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 11.789s | Local Loss: 0.1311 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.274s | Local Loss: 0.2263 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.169s | Local Loss: 0.3826 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.588s | Local Loss: 0.0346 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 13.135s | Local Loss: 0.0315 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 11.556s | Local Loss: 0.3546 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 11.696s | Local Loss: 0.0696 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.159s | Local Loss: 0.0481 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.186s | Local Loss: 0.0141 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 11.665s | Local Loss: 0.4880 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.135s | Local Loss: 0.3875 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 11.918s | Local Loss: 0.5516 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 11.721s | Local Loss: 0.0272 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 11.898s | Local Loss: 0.0576 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.523s | Local Loss: 0.3149 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 11.882s | Local Loss: 0.2537 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 12.898s | Local Loss: 0.1706 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 11.817s | Local Loss: 0.0816 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 11.820s | Local Loss: 0.0293 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 11.819s | Local Loss: 0.0406 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 11.786s | Local Loss: 0.3535 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 11.719s | Local Loss: 0.2113 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 11.887s | Local Loss: 0.1313 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 20.834s | Local Loss: 0.0657 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 15.446s | Local Loss: 0.0625 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 15.581s | Local Loss: 0.0341 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 13.788s | Local Loss: 0.1755 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 13.354s | Local Loss: 0.0466 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 13.760s | Local Loss: 0.1310 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 14.830s | Local Loss: 0.0705 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 13.070s | Local Loss: 0.1655 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 12.794s | Local Loss: 0.3132 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 13.288s | Local Loss: 0.0225 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 13.312s | Local Loss: 0.0729 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 12.949s | Local Loss: 0.0327 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 13.416s | Local Loss: 0.0311 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 12.914s | Local Loss: 0.2468 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 12.977s | Local Loss: 0.4734 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 13.113s | Local Loss: 0.1191 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 12.922s | Local Loss: 0.0196 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 13.248s | Local Loss: 0.0000 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 12.599s | Local Loss: 0.0416 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 12.890s | Local Loss: 0.1371 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 12.861s | Local Loss: 0.1533 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 12.976s | Local Loss: 0.0420 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 13.322s | Local Loss: 0.1545 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 13.045s | Local Loss: 0.0279 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 13.076s | Local Loss: 0.1234 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 13.301s | Local Loss: 0.3855 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 12.822s | Local Loss: 0.0628 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 13.040s | Local Loss: 0.0330 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 13.397s | Local Loss: 0.0766 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 12.945s | Local Loss: 0.1404 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 12.858s | Local Loss: 0.0431 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 12.847s | Local Loss: 0.2246 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 12.644s | Local Loss: 0.0530 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 13.257s | Local Loss: 0.5507 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 12.784s | Local Loss: 0.1531 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 12.746s | Local Loss: 0.0720 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 13.223s | Local Loss: 0.4597 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 12.748s | Local Loss: 0.1358 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 13.072s | Local Loss: 0.1397 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 12.726s | Local Loss: 0.0394 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 12.604s | Local Loss: 0.3431 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 13.390s | Local Loss: 0.1431 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 12.746s | Local Loss: 0.1375 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 13.058s | Local Loss: 0.0482 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 13.145s | Local Loss: 0.2041 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 12.911s | Local Loss: 0.0733 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 13.285s | Local Loss: 0.0236 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 12.744s | Local Loss: 0.0314 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 12.848s | Local Loss: 0.0259 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 13.224s | Local Loss: 0.0332 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 19.998s | Local Loss: 0.0309 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 16.944s | Local Loss: 0.5233 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 17.985s | Local Loss: 0.0160 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 16.239s | Local Loss: 0.2171 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 17.042s | Local Loss: 0.2930 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 17.509s | Local Loss: 0.0059 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 17.112s | Local Loss: 0.3777 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 18.338s | Local Loss: 0.0420 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 13.572s | Local Loss: 0.1287 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 14.069s | Local Loss: 0.0253 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 13.420s | Local Loss: 0.0595 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 13.564s | Local Loss: 0.2663 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 13.502s | Local Loss: 0.1376 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 13.268s | Local Loss: 0.0591 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 13.570s | Local Loss: 0.1826 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 13.391s | Local Loss: 0.0606 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 13.403s | Local Loss: 0.0940 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 13.580s | Local Loss: 0.3046 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 14.298s | Local Loss: 0.0756 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 13.301s | Local Loss: 0.0211 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 13.594s | Local Loss: 0.1555 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 13.434s | Local Loss: 0.3564 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 13.114s | Local Loss: 0.0543 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 13.228s | Local Loss: 0.0780 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 13.222s | Local Loss: 0.1449 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 14.128s | Local Loss: 0.0488 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 13.605s | Local Loss: 0.0582 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 13.245s | Local Loss: 0.3246 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 13.889s | Local Loss: 0.0616 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 13.752s | Local Loss: 0.0368 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 13.282s | Local Loss: 0.0589 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 13.719s | Local Loss: 0.0362 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 13.440s | Local Loss: 0.2855 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 13.893s | Local Loss: 0.1483 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 13.647s | Local Loss: 0.0914 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 13.066s | Local Loss: 0.0452 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 13.834s | Local Loss: 0.0344 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 13.489s | Local Loss: 0.3973 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 13.391s | Local Loss: 0.0128 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 13.757s | Local Loss: 0.0356 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 13.922s | Local Loss: 0.1170 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 13.902s | Local Loss: 0.1597 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 13.819s | Local Loss: 0.1080 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 13.130s | Local Loss: 0.0641 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 13.890s | Local Loss: 0.0560 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 13.406s | Local Loss: 0.0469 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 13.483s | Local Loss: 0.0108 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 13.301s | Local Loss: 0.0447 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 13.400s | Local Loss: 0.0223 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 13.749s | Local Loss: 0.0567 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 19.684s | Local Loss: 0.1045 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 16.841s | Local Loss: 0.4259 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 17.833s | Local Loss: 0.0523 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 16.682s | Local Loss: 0.0446 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 17.685s | Local Loss: 0.0137 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 16.466s | Local Loss: 0.4152 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 17.105s | Local Loss: 0.0783 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 17.395s | Local Loss: 0.1108 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 17.841s | Local Loss: 0.2256 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 16.679s | Local Loss: 0.1737 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 19.156s | Local Loss: 0.1501 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 13.092s | Local Loss: 0.0597 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 14.031s | Local Loss: 0.0555 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 13.513s | Local Loss: 0.3735 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 13.568s | Local Loss: 0.0802 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 13.586s | Local Loss: 0.0314 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 12.997s | Local Loss: 0.0354 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 13.638s | Local Loss: 0.0176 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 13.344s | Local Loss: 0.0333 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 13.125s | Local Loss: 0.0358 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 13.367s | Local Loss: 0.1942 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 12.938s | Local Loss: 0.0268 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 13.132s | Local Loss: 0.0574 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 12.874s | Local Loss: 0.0462 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 13.200s | Local Loss: 0.0288 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 13.536s | Local Loss: 0.0752 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 13.304s | Local Loss: 0.0107 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 12.988s | Local Loss: 0.1248 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 13.762s | Local Loss: 0.4984 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 13.614s | Local Loss: 0.4483 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 13.289s | Local Loss: 0.0497 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 13.603s | Local Loss: 0.1369 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 13.795s | Local Loss: 0.3554 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 13.681s | Local Loss: 0.0280 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 13.776s | Local Loss: 0.3185 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 13.367s | Local Loss: 0.4238 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 13.591s | Local Loss: 0.1258 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 13.496s | Local Loss: 0.2350 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 13.182s | Local Loss: 0.0648 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 13.659s | Local Loss: 0.0480 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 13.430s | Local Loss: 0.0285 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 13.247s | Local Loss: 0.0276 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 13.775s | Local Loss: 0.0416 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 13.544s | Local Loss: 0.0742 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 13.479s | Local Loss: 0.0176 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 13.203s | Local Loss: 0.2202 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 13.179s | Local Loss: 0.1226 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 13.852s | Local Loss: 0.0158 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 13.811s | Local Loss: 0.0084 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 13.114s | Local Loss: 0.0263 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 17.925s | Local Loss: 0.0097 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 15.194s | Local Loss: 0.0813 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 15.091s | Local Loss: 0.1417 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 15.282s | Local Loss: 0.0439 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 15.294s | Local Loss: 0.0302 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 14.993s | Local Loss: 0.0338 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 15.462s | Local Loss: 0.0385 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 15.277s | Local Loss: 0.0960 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 14.652s | Local Loss: 0.2840 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 14.988s | Local Loss: 0.0091 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 15.349s | Local Loss: 0.1259 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 15.106s | Local Loss: 0.0239 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 14.802s | Local Loss: 0.0199 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 15.168s | Local Loss: 0.0285 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 15.130s | Local Loss: 0.1022 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 14.910s | Local Loss: 0.0277 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 14.496s | Local Loss: 0.0589 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 16.454s | Local Loss: 0.0366 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 13.422s | Local Loss: 0.1093 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 13.629s | Local Loss: 0.0266 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 13.537s | Local Loss: 0.1258 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 13.359s | Local Loss: 0.2895 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 13.302s | Local Loss: 0.0000 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 13.528s | Local Loss: 0.4398 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 13.187s | Local Loss: 0.0131 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 13.547s | Local Loss: 0.0671 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 13.487s | Local Loss: 0.0258 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 13.355s | Local Loss: 0.1068 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 13.371s | Local Loss: 0.0272 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 13.368s | Local Loss: 0.2211 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 13.196s | Local Loss: 0.0341 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 13.393s | Local Loss: 0.1122 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 13.504s | Local Loss: 0.0215 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 13.538s | Local Loss: 0.2539 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 13.303s | Local Loss: 0.0338 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 13.569s | Local Loss: 0.0253 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 13.449s | Local Loss: 0.0323 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 13.505s | Local Loss: 0.3613 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 13.755s | Local Loss: 0.2112 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 13.190s | Local Loss: 0.0265 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 13.208s | Local Loss: 0.0729 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 13.858s | Local Loss: 0.0178 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 13.334s | Local Loss: 0.0259 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 13.270s | Local Loss: 0.0565 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 13.825s | Local Loss: 0.2887 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 13.502s | Local Loss: 0.3696 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 13.430s | Local Loss: 0.0173 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 13.711s | Local Loss: 0.0445 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 13.539s | Local Loss: 0.0162 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 13.480s | Local Loss: 0.2532 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 19.856s | Local Loss: 0.1348 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 17.172s | Local Loss: 0.0262 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 17.602s | Local Loss: 0.1886 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 17.274s | Local Loss: 0.0915 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 17.329s | Local Loss: 0.0483 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 18.441s | Local Loss: 0.0920 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 14.307s | Local Loss: 0.0106 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 13.617s | Local Loss: 0.0295 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 13.478s | Local Loss: 0.1615 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 13.743s | Local Loss: 0.0669 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 13.417s | Local Loss: 0.3895 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 13.731s | Local Loss: 0.0514 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 13.578s | Local Loss: 0.0338 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 13.966s | Local Loss: 0.0341 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 13.563s | Local Loss: 0.0511 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 13.288s | Local Loss: 0.0132 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 13.327s | Local Loss: 0.2095 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 14.639s | Local Loss: 0.3228 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 13.227s | Local Loss: 0.0544 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 12.883s | Local Loss: 0.0375 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 13.460s | Local Loss: 0.0263 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 13.409s | Local Loss: 0.0504 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 21.251s | Local Loss: 0.0236 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 20.759s | Local Loss: 0.0622 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 22.734s | Local Loss: 0.0309 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 20.517s | Local Loss: 0.0268 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 20.599s | Local Loss: 0.0646 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 21.001s | Local Loss: 0.0436 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 24.878s | Local Loss: 0.0153 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 20.048s | Local Loss: 0.0309 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 20.976s | Local Loss: 0.0269 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 20.255s | Local Loss: 0.0216 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 21.611s | Local Loss: 0.0189 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 20.178s | Local Loss: 0.0293 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 20.892s | Local Loss: 0.0323 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 23.866s | Local Loss: 0.0228 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 21.687s | Local Loss: 0.0993 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 19.945s | Local Loss: 0.1342 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 22.410s | Local Loss: 0.0249 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 20.835s | Local Loss: 0.0456 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 25.188s | Local Loss: 0.0058 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 19.901s | Local Loss: 0.0129 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 21.456s | Local Loss: 0.0624 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 20.749s | Local Loss: 0.0166 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 21.398s | Local Loss: 0.0119 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 15.923s | Local Loss: 0.0142 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 13.225s | Local Loss: 0.0677 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 13.452s | Local Loss: 0.0184 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 13.564s | Local Loss: 0.0937 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 13.560s | Local Loss: 0.0197 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 17.952s | Local Loss: 0.0605 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 15.439s | Local Loss: 0.0127 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 15.497s | Local Loss: 0.4252 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 15.105s | Local Loss: 0.0598 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 14.877s | Local Loss: 0.1185 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 15.416s | Local Loss: 0.0266 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 15.233s | Local Loss: 0.0141 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 15.185s | Local Loss: 0.1797 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 14.958s | Local Loss: 0.0146 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 15.014s | Local Loss: 0.2684 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 16.009s | Local Loss: 0.0454 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 13.627s | Local Loss: 0.0219 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 13.469s | Local Loss: 0.0620 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 13.313s | Local Loss: 0.0207 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 13.074s | Local Loss: 0.0362 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 13.465s | Local Loss: 0.0211 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 13.968s | Local Loss: 0.0216 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 13.382s | Local Loss: 0.1525 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 13.294s | Local Loss: 0.0306 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 13.524s | Local Loss: 0.0178 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 13.593s | Local Loss: 0.0153 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 14.116s | Local Loss: 0.0280 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 14.473s | Local Loss: 0.0102 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 14.233s | Local Loss: 0.0381 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 14.181s | Local Loss: 0.0155 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 14.404s | Local Loss: 0.0187 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 14.264s | Local Loss: 0.0149 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 13.706s | Local Loss: 0.0113 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 13.977s | Local Loss: 0.0442 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 13.411s | Local Loss: 0.0695 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 14.002s | Local Loss: 0.0128 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 13.620s | Local Loss: 0.0197 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 13.876s | Local Loss: 0.0249 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 13.773s | Local Loss: 0.0175 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 13.613s | Local Loss: 0.0150 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 13.554s | Local Loss: 0.0158 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 14.206s | Local Loss: 0.0973 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 13.331s | Local Loss: 0.0187 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 13.347s | Local Loss: 0.0186 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 14.157s | Local Loss: 0.0164 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 13.240s | Local Loss: 0.2770 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 13.405s | Local Loss: 0.0548 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 13.401s | Local Loss: 0.0165 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 13.617s | Local Loss: 0.0169 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 13.967s | Local Loss: 0.0258 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 14.197s | Local Loss: 0.0668 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 13.631s | Local Loss: 0.0174 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 14.099s | Local Loss: 0.0121 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 13.596s | Local Loss: 0.0141 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 13.643s | Local Loss: 0.0180 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 19.864s | Local Loss: 0.0237 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 17.500s | Local Loss: 0.0233 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 16.739s | Local Loss: 0.3047 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 17.894s | Local Loss: 0.0295 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 16.944s | Local Loss: 0.0306 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 18.899s | Local Loss: 0.0148 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 17.063s | Local Loss: 0.0660 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 18.118s | Local Loss: 0.0309 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 17.202s | Local Loss: 0.0481 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 17.556s | Local Loss: 0.0549 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 17.846s | Local Loss: 0.0327 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 17.558s | Local Loss: 0.0538 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 16.594s | Local Loss: 0.0287 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 25.038s | Local Loss: 0.0262 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 20.233s | Local Loss: 0.0188 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 18.748s | Local Loss: 0.0364 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 14.175s | Local Loss: 0.1341 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 13.417s | Local Loss: 0.0188 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 12.943s | Local Loss: 0.0000 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 13.162s | Local Loss: 0.2057 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 13.168s | Local Loss: 0.0434 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 12.462s | Local Loss: 0.0133 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 13.136s | Local Loss: 0.0152 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 12.844s | Local Loss: 0.0109 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 12.732s | Local Loss: 0.0359 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 13.140s | Local Loss: 0.1651 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 12.766s | Local Loss: 0.0502 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 12.934s | Local Loss: 0.0258 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 13.288s | Local Loss: 0.0151 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 12.673s | Local Loss: 0.0192 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 12.928s | Local Loss: 0.0070 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 12.990s | Local Loss: 0.0497 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 13.037s | Local Loss: 0.0249 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 12.833s | Local Loss: 0.1009 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 12.783s | Local Loss: 0.0772 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 13.196s | Local Loss: 0.0169 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 12.452s | Local Loss: 0.0036 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 13.049s | Local Loss: 0.0226 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 12.864s | Local Loss: 0.0171 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 13.063s | Local Loss: 0.0140 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 13.013s | Local Loss: 0.0375 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 13.648s | Local Loss: 0.2865 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 16.151s | Local Loss: 0.0185 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 14.048s | Local Loss: 0.0945 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 13.959s | Local Loss: 0.0271 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 13.149s | Local Loss: 0.0273 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 14.124s | Local Loss: 0.0090 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_4.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_4.log
new file mode 100644
index 0000000000000000000000000000000000000000..e19bfbbe9f4f3ffda31cd61c420376e862a04ee9
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_4.log
@@ -0,0 +1,900 @@
+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 17.626s | Local Loss: 0.4279 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 14.316s | Local Loss: 0.0160 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 13.493s | Local Loss: 0.4169 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 12.685s | Local Loss: 0.4239 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 12.101s | Local Loss: 0.1294 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 14.525s | Local Loss: 0.2675 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 23.130s | Local Loss: 0.5372 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 13.858s | Local Loss: 0.0463 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 14.212s | Local Loss: 0.0825 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 13.083s | Local Loss: 0.3333 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 13.725s | Local Loss: 0.3448 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 14.081s | Local Loss: 0.1838 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 14.795s | Local Loss: 0.2314 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 15.017s | Local Loss: 0.1334 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 13.240s | Local Loss: 0.1559 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 12.690s | Local Loss: 0.0343 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 13.679s | Local Loss: 0.0899 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 14.205s | Local Loss: 0.1627 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 13.267s | Local Loss: 0.1149 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 13.789s | Local Loss: 0.2977 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 12.347s | Local Loss: 0.1040 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 13.325s | Local Loss: 0.2923 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 13.340s | Local Loss: 0.5723 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 11.805s | Local Loss: 0.0847 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 12.388s | Local Loss: 0.1159 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 12.158s | Local Loss: 0.0642 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 11.883s | Local Loss: 0.0904 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 13.583s | Local Loss: 0.1889 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 12.154s | Local Loss: 0.0651 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 13.305s | Local Loss: 0.0932 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 12.900s | Local Loss: 0.0796 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 12.197s | Local Loss: 0.3968 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 12.672s | Local Loss: 0.1876 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 13.883s | Local Loss: 0.1698 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 12.636s | Local Loss: 0.0703 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 12.567s | Local Loss: 0.4307 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 13.640s | Local Loss: 0.4847 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 12.264s | Local Loss: 0.2727 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 12.051s | Local Loss: 0.3875 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 12.916s | Local Loss: 0.2445 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 13.412s | Local Loss: 0.0000 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 13.162s | Local Loss: 0.2346 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 13.319s | Local Loss: 0.2380 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 12.895s | Local Loss: 0.0373 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 12.378s | Local Loss: 0.0563 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 12.502s | Local Loss: 0.0572 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 12.659s | Local Loss: 0.4385 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 12.405s | Local Loss: 0.3763 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 12.992s | Local Loss: 0.3873 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 12.733s | Local Loss: 0.0405 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 20.836s | Local Loss: 0.0639 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 17.346s | Local Loss: 0.5529 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 16.725s | Local Loss: 0.1139 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 17.120s | Local Loss: 0.2230 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 13.952s | Local Loss: 0.0646 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 13.465s | Local Loss: 0.1173 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 13.515s | Local Loss: 0.1386 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 13.991s | Local Loss: 0.0452 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 12.839s | Local Loss: 0.0157 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 13.149s | Local Loss: 0.1633 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 13.978s | Local Loss: 0.3415 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 14.819s | Local Loss: 0.0785 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 14.441s | Local Loss: 0.3679 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 13.718s | Local Loss: 0.5087 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 15.094s | Local Loss: 0.0124 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 14.432s | Local Loss: 0.0544 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 15.220s | Local Loss: 0.3009 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 13.414s | Local Loss: 0.2534 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 14.185s | Local Loss: 0.1635 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 14.023s | Local Loss: 0.2813 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 13.711s | Local Loss: 0.4956 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 13.282s | Local Loss: 0.3661 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 13.359s | Local Loss: 0.0832 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 13.998s | Local Loss: 0.0368 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 13.462s | Local Loss: 0.4903 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 14.604s | Local Loss: 0.2087 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 13.506s | Local Loss: 0.0735 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 13.639s | Local Loss: 0.0679 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 13.677s | Local Loss: 0.0794 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 13.089s | Local Loss: 0.0276 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 13.329s | Local Loss: 0.1424 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 13.571s | Local Loss: 0.2905 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 13.005s | Local Loss: 0.0384 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 13.952s | Local Loss: 0.2686 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 13.447s | Local Loss: 0.0407 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 12.838s | Local Loss: 0.0578 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 13.726s | Local Loss: 0.0317 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 13.356s | Local Loss: 0.0113 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 13.480s | Local Loss: 0.0594 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 13.225s | Local Loss: 0.0204 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 13.586s | Local Loss: 0.1712 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 13.658s | Local Loss: 0.2500 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 13.246s | Local Loss: 0.0250 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 13.971s | Local Loss: 0.1285 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 13.149s | Local Loss: 0.2833 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 13.399s | Local Loss: 0.0516 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 13.204s | Local Loss: 0.0058 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 14.169s | Local Loss: 0.0317 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 13.210s | Local Loss: 0.3115 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 13.476s | Local Loss: 0.3290 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 20.476s | Local Loss: 0.4353 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 17.121s | Local Loss: 0.1109 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 14.605s | Local Loss: 0.0064 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 14.803s | Local Loss: 0.3503 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 15.052s | Local Loss: 0.2553 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 16.032s | Local Loss: 0.0501 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 16.002s | Local Loss: 0.0948 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 14.769s | Local Loss: 0.0526 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 14.751s | Local Loss: 0.0344 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 16.113s | Local Loss: 0.0917 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 14.940s | Local Loss: 0.3411 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 16.117s | Local Loss: 0.0247 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 13.963s | Local Loss: 0.2110 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 13.341s | Local Loss: 0.3922 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 13.644s | Local Loss: 0.1046 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 13.452s | Local Loss: 0.0140 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 13.869s | Local Loss: 0.0414 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 13.661s | Local Loss: 0.1769 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 13.459s | Local Loss: 0.0735 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 13.682s | Local Loss: 0.2937 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 13.190s | Local Loss: 0.1573 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 13.667s | Local Loss: 0.0353 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 13.498s | Local Loss: 0.3341 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 13.669s | Local Loss: 0.1988 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 14.435s | Local Loss: 0.0333 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 13.131s | Local Loss: 0.4126 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 13.563s | Local Loss: 0.1207 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 13.614s | Local Loss: 0.0596 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 13.126s | Local Loss: 0.0200 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 14.403s | Local Loss: 0.0271 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 13.277s | Local Loss: 0.0483 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 13.832s | Local Loss: 0.1415 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 13.328s | Local Loss: 0.0597 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 13.218s | Local Loss: 0.0460 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 14.008s | Local Loss: 0.1750 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 14.190s | Local Loss: 0.0488 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 13.641s | Local Loss: 0.0631 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 14.503s | Local Loss: 0.0222 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 13.743s | Local Loss: 0.3373 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 14.448s | Local Loss: 0.1553 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 14.244s | Local Loss: 0.2736 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 14.494s | Local Loss: 0.0202 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 13.430s | Local Loss: 0.1280 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 13.840s | Local Loss: 0.0883 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 13.614s | Local Loss: 0.1068 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 13.941s | Local Loss: 0.0327 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 13.397s | Local Loss: 0.2469 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 13.913s | Local Loss: 0.4736 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 14.062s | Local Loss: 0.2033 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 13.533s | Local Loss: 0.0212 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 21.851s | Local Loss: 0.0822 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 16.666s | Local Loss: 0.2384 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 17.577s | Local Loss: 0.2535 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 17.938s | Local Loss: 0.0216 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 17.918s | Local Loss: 0.0000 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 16.978s | Local Loss: 0.0421 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 16.915s | Local Loss: 0.0162 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 18.072s | Local Loss: 0.0659 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 17.535s | Local Loss: 0.0929 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 17.006s | Local Loss: 0.4499 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 16.480s | Local Loss: 0.0496 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 16.506s | Local Loss: 0.0580 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 17.371s | Local Loss: 0.0155 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 17.413s | Local Loss: 0.3606 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 16.995s | Local Loss: 0.0202 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 16.585s | Local Loss: 0.0396 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 17.103s | Local Loss: 0.0206 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 18.200s | Local Loss: 0.0570 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 14.384s | Local Loss: 0.0436 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 13.613s | Local Loss: 0.0662 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 14.653s | Local Loss: 0.0954 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 14.100s | Local Loss: 0.0112 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 13.317s | Local Loss: 0.3355 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 14.115s | Local Loss: 0.0771 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 14.164s | Local Loss: 0.1000 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 13.386s | Local Loss: 0.1147 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 13.495s | Local Loss: 0.3201 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 13.727s | Local Loss: 0.0432 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 15.067s | Local Loss: 0.0265 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 15.822s | Local Loss: 0.0382 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 14.068s | Local Loss: 0.1353 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 14.450s | Local Loss: 0.0601 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 14.147s | Local Loss: 0.0270 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 13.897s | Local Loss: 0.0826 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 13.940s | Local Loss: 0.0293 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 13.965s | Local Loss: 0.1158 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 13.810s | Local Loss: 0.2415 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 13.878s | Local Loss: 0.0399 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 13.993s | Local Loss: 0.0441 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 13.720s | Local Loss: 0.0310 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 13.735s | Local Loss: 0.3026 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 13.643s | Local Loss: 0.0268 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 13.980s | Local Loss: 0.0392 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 16.638s | Local Loss: 0.0359 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 17.358s | Local Loss: 0.0291 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 16.308s | Local Loss: 0.1689 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 15.625s | Local Loss: 0.0298 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 15.999s | Local Loss: 0.3929 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 13.897s | Local Loss: 0.0316 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 14.477s | Local Loss: 0.0332 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 22.092s | Local Loss: 0.0269 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 17.444s | Local Loss: 0.0733 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 16.559s | Local Loss: 0.0689 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 18.298s | Local Loss: 0.1851 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 14.211s | Local Loss: 0.0444 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 13.970s | Local Loss: 0.0439 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 13.313s | Local Loss: 0.0221 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 14.690s | Local Loss: 0.0283 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 13.665s | Local Loss: 0.0289 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 13.300s | Local Loss: 0.0275 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 13.620s | Local Loss: 0.4826 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 22.565s | Local Loss: 0.0611 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 20.911s | Local Loss: 0.0244 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 14.195s | Local Loss: 0.0329 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 14.078s | Local Loss: 0.1216 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 16.822s | Local Loss: 0.0310 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 14.418s | Local Loss: 0.0314 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 15.361s | Local Loss: 0.0308 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 13.884s | Local Loss: 0.2905 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 15.655s | Local Loss: 0.0532 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 13.699s | Local Loss: 0.0648 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 14.238s | Local Loss: 0.0446 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 14.123s | Local Loss: 0.3065 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 14.149s | Local Loss: 0.0546 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 14.885s | Local Loss: 0.0140 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 14.052s | Local Loss: 0.0476 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 13.820s | Local Loss: 0.1961 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 13.949s | Local Loss: 0.0253 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 14.333s | Local Loss: 0.0157 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 14.875s | Local Loss: 0.1854 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 14.104s | Local Loss: 0.0054 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 14.730s | Local Loss: 0.1240 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 13.666s | Local Loss: 0.0235 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 14.298s | Local Loss: 0.3305 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 13.817s | Local Loss: 0.1070 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 13.796s | Local Loss: 0.0351 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 14.716s | Local Loss: 0.0254 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 13.509s | Local Loss: 0.3078 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 14.548s | Local Loss: 0.0274 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 14.250s | Local Loss: 0.0410 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 13.946s | Local Loss: 0.2494 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 14.538s | Local Loss: 0.0079 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 13.544s | Local Loss: 0.1307 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 14.085s | Local Loss: 0.1162 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 14.532s | Local Loss: 0.0321 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 14.102s | Local Loss: 0.0301 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 14.922s | Local Loss: 0.0331 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 13.517s | Local Loss: 0.0872 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 14.866s | Local Loss: 0.0534 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 14.184s | Local Loss: 0.0893 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 21.758s | Local Loss: 0.0169 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 15.914s | Local Loss: 0.0256 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 15.086s | Local Loss: 0.0721 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 17.104s | Local Loss: 0.0387 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 14.787s | Local Loss: 0.0295 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 15.581s | Local Loss: 0.0352 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 14.877s | Local Loss: 0.0116 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 16.216s | Local Loss: 0.0466 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 16.250s | Local Loss: 0.3906 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 13.596s | Local Loss: 0.4512 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 14.920s | Local Loss: 0.0828 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 13.562s | Local Loss: 0.0565 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 13.634s | Local Loss: 0.1746 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 14.868s | Local Loss: 0.0501 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 13.329s | Local Loss: 0.0259 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 14.752s | Local Loss: 0.0428 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 13.729s | Local Loss: 0.0321 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 13.341s | Local Loss: 0.0149 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 14.967s | Local Loss: 0.0608 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 13.421s | Local Loss: 0.0000 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 13.355s | Local Loss: 0.0417 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 14.107s | Local Loss: 0.0137 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 13.753s | Local Loss: 0.0157 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 14.309s | Local Loss: 0.0556 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 13.439s | Local Loss: 0.0114 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 13.536s | Local Loss: 0.0640 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 14.686s | Local Loss: 0.2664 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 13.541s | Local Loss: 0.0316 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 14.945s | Local Loss: 0.0575 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 14.293s | Local Loss: 0.0392 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 13.622s | Local Loss: 0.2038 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 15.095s | Local Loss: 0.0575 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 13.513s | Local Loss: 0.0346 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 13.894s | Local Loss: 0.1557 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 15.692s | Local Loss: 0.0218 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 13.206s | Local Loss: 0.4322 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 13.961s | Local Loss: 0.0324 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 14.132s | Local Loss: 0.0225 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 13.986s | Local Loss: 0.0141 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 14.088s | Local Loss: 0.1336 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 13.445s | Local Loss: 0.2664 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 14.298s | Local Loss: 0.0165 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 14.665s | Local Loss: 0.0468 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 14.087s | Local Loss: 0.0477 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 13.946s | Local Loss: 0.1045 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 13.711s | Local Loss: 0.0197 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 13.867s | Local Loss: 0.0223 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 14.685s | Local Loss: 0.0166 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 14.285s | Local Loss: 0.1305 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 14.319s | Local Loss: 0.0123 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 24.506s | Local Loss: 0.2427 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 19.034s | Local Loss: 0.0466 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 17.219s | Local Loss: 0.0125 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 19.265s | Local Loss: 0.0675 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 18.738s | Local Loss: 0.0290 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 20.602s | Local Loss: 0.0209 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 17.445s | Local Loss: 0.0271 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 19.045s | Local Loss: 0.2471 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 17.551s | Local Loss: 0.1238 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 18.581s | Local Loss: 0.0164 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 17.602s | Local Loss: 0.0170 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 17.322s | Local Loss: 0.1230 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 17.544s | Local Loss: 0.0160 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 17.211s | Local Loss: 0.0446 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 18.547s | Local Loss: 0.0654 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 13.579s | Local Loss: 0.0463 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 13.774s | Local Loss: 0.0949 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 14.322s | Local Loss: 0.0227 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 14.458s | Local Loss: 0.0229 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 14.161s | Local Loss: 0.0487 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 13.353s | Local Loss: 0.0154 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 13.736s | Local Loss: 0.1446 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 14.326s | Local Loss: 0.0181 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 16.765s | Local Loss: 0.0216 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 15.663s | Local Loss: 0.0099 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 15.409s | Local Loss: 0.0647 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 16.965s | Local Loss: 0.0251 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 14.902s | Local Loss: 0.1500 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 14.158s | Local Loss: 0.0161 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 14.865s | Local Loss: 0.0034 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 15.144s | Local Loss: 0.0087 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 15.175s | Local Loss: 0.0750 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 14.545s | Local Loss: 0.0469 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 13.783s | Local Loss: 0.0167 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 15.266s | Local Loss: 0.0166 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 13.946s | Local Loss: 0.1030 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 14.331s | Local Loss: 0.0128 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 14.946s | Local Loss: 0.3770 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 14.682s | Local Loss: 0.4075 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 16.468s | Local Loss: 0.0293 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 15.718s | Local Loss: 0.0310 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 15.328s | Local Loss: 0.0854 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 14.658s | Local Loss: 0.1111 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 14.534s | Local Loss: 0.0906 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 14.329s | Local Loss: 0.2929 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 13.734s | Local Loss: 0.0273 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 14.071s | Local Loss: 0.0256 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 13.754s | Local Loss: 0.0270 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 14.314s | Local Loss: 0.0433 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 13.748s | Local Loss: 0.0182 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 19.091s | Local Loss: 0.0320 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 16.899s | Local Loss: 0.0456 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 17.505s | Local Loss: 0.0150 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 16.688s | Local Loss: 0.0225 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 17.252s | Local Loss: 0.0961 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 16.815s | Local Loss: 0.0369 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 15.348s | Local Loss: 0.0821 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 16.825s | Local Loss: 0.0126 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 14.717s | Local Loss: 0.0155 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 19.467s | Local Loss: 0.0204 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 24.341s | Local Loss: 0.0155 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 17.240s | Local Loss: 0.0267 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 15.326s | Local Loss: 0.0136 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 15.238s | Local Loss: 0.0226 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 15.293s | Local Loss: 0.0099 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 15.123s | Local Loss: 0.0174 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 15.575s | Local Loss: 0.2440 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 15.427s | Local Loss: 0.0887 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 15.432s | Local Loss: 0.0171 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 13.849s | Local Loss: 0.0232 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 13.238s | Local Loss: 0.0312 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 13.130s | Local Loss: 0.0427 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 13.851s | Local Loss: 0.0272 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 14.287s | Local Loss: 0.0333 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 14.788s | Local Loss: 0.0144 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 14.343s | Local Loss: 0.0527 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 13.825s | Local Loss: 0.0137 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 13.533s | Local Loss: 0.0395 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 13.182s | Local Loss: 0.0306 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 14.990s | Local Loss: 0.0200 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 13.027s | Local Loss: 0.0129 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 13.764s | Local Loss: 0.0569 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 13.407s | Local Loss: 0.0511 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 13.421s | Local Loss: 0.0222 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 14.773s | Local Loss: 0.0526 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 13.283s | Local Loss: 0.0060 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 13.027s | Local Loss: 0.0343 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 14.564s | Local Loss: 0.0096 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 13.421s | Local Loss: 0.0100 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 13.592s | Local Loss: 0.0077 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 13.659s | Local Loss: 0.0897 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 13.383s | Local Loss: 0.0301 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 13.935s | Local Loss: 0.0122 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 13.220s | Local Loss: 0.1343 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 13.274s | Local Loss: 0.0167 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 13.942s | Local Loss: 0.0420 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 13.089s | Local Loss: 0.0141 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 14.384s | Local Loss: 0.0411 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 13.078s | Local Loss: 0.0253 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 13.316s | Local Loss: 0.0301 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 21.700s | Local Loss: 0.0300 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 17.447s | Local Loss: 0.0215 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 16.888s | Local Loss: 0.2611 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 17.402s | Local Loss: 0.0497 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 16.926s | Local Loss: 0.0214 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 17.638s | Local Loss: 0.0152 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 18.262s | Local Loss: 0.0284 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 14.533s | Local Loss: 0.0236 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 13.146s | Local Loss: 0.0828 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 14.325s | Local Loss: 0.0123 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 13.713s | Local Loss: 0.0303 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 13.388s | Local Loss: 0.0092 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 13.372s | Local Loss: 0.0767 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 14.118s | Local Loss: 0.0111 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 13.954s | Local Loss: 0.0105 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 13.469s | Local Loss: 0.0130 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 14.251s | Local Loss: 0.2281 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 13.741s | Local Loss: 0.0237 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 13.238s | Local Loss: 0.0214 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 14.439s | Local Loss: 0.1079 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 14.214s | Local Loss: 0.0162 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 13.413s | Local Loss: 0.0706 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 13.440s | Local Loss: 0.0083 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 13.165s | Local Loss: 0.0270 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 13.473s | Local Loss: 0.0880 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 13.669s | Local Loss: 0.0280 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 13.036s | Local Loss: 0.0105 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 13.342s | Local Loss: 0.0121 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 13.708s | Local Loss: 0.0265 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 13.870s | Local Loss: 0.0098 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 13.716s | Local Loss: 0.0258 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 13.635s | Local Loss: 0.0242 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 14.127s | Local Loss: 0.0083 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 13.366s | Local Loss: 0.0069 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 14.630s | Local Loss: 0.0107 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 13.063s | Local Loss: 0.0648 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 13.569s | Local Loss: 0.0105 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 13.479s | Local Loss: 0.0173 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 13.450s | Local Loss: 0.0814 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 13.811s | Local Loss: 0.0070 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 13.051s | Local Loss: 0.0138 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 14.039s | Local Loss: 0.0224 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 13.816s | Local Loss: 0.0574 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 13.186s | Local Loss: 0.0000 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 13.749s | Local Loss: 0.0103 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 13.362s | Local Loss: 0.0055 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 14.129s | Local Loss: 0.0167 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 13.482s | Local Loss: 0.0116 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 13.712s | Local Loss: 0.0414 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 14.474s | Local Loss: 0.0299 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 21.289s | Local Loss: 0.0070 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 17.364s | Local Loss: 0.1028 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 16.275s | Local Loss: 0.0079 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 17.109s | Local Loss: 0.0189 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 17.117s | Local Loss: 0.0189 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 16.923s | Local Loss: 0.0642 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 18.593s | Local Loss: 0.0437 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 18.621s | Local Loss: 0.0137 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 18.134s | Local Loss: 0.0069 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 17.416s | Local Loss: 0.1067 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 17.878s | Local Loss: 0.0773 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 18.377s | Local Loss: 0.0110 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 14.564s | Local Loss: 0.0086 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 13.701s | Local Loss: 0.3487 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 13.926s | Local Loss: 0.0079 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 14.132s | Local Loss: 0.0065 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 13.465s | Local Loss: 0.0057 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 13.757s | Local Loss: 0.0231 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 15.647s | Local Loss: 0.0085 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 13.336s | Local Loss: 0.0226 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 14.130s | Local Loss: 0.0110 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 14.200s | Local Loss: 0.1086 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 13.574s | Local Loss: 0.0133 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 14.420s | Local Loss: 0.0375 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 13.164s | Local Loss: 0.0692 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 13.914s | Local Loss: 0.0092 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 13.782s | Local Loss: 0.0929 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 14.646s | Local Loss: 0.0105 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 13.328s | Local Loss: 0.0060 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 14.314s | Local Loss: 0.0038 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 14.887s | Local Loss: 0.0142 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 14.237s | Local Loss: 0.0651 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 14.438s | Local Loss: 0.0191 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 14.555s | Local Loss: 0.0310 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 14.501s | Local Loss: 0.0347 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 13.743s | Local Loss: 0.0098 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 14.779s | Local Loss: 0.0043 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 14.697s | Local Loss: 0.1791 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 13.828s | Local Loss: 0.0086 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 14.372s | Local Loss: 0.0132 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 14.312s | Local Loss: 0.0244 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 13.573s | Local Loss: 0.0082 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 13.368s | Local Loss: 0.0176 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 15.819s | Local Loss: 0.0118 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 13.279s | Local Loss: 0.0213 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 13.874s | Local Loss: 0.0101 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 13.888s | Local Loss: 0.0361 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 13.584s | Local Loss: 0.0130 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 14.318s | Local Loss: 0.0714 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 13.494s | Local Loss: 0.1280 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 22:10:06] Logger closing.
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 14.864s | Local Loss: 0.4279 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 14.227s | Local Loss: 0.0160 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.454s | Local Loss: 0.4169 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.132s | Local Loss: 0.4239 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 13.038s | Local Loss: 0.1295 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 12.128s | Local Loss: 0.2678 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.462s | Local Loss: 0.5378 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 12.600s | Local Loss: 0.0461 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.963s | Local Loss: 0.0829 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 15.188s | Local Loss: 0.3340 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 14.238s | Local Loss: 0.3452 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.209s | Local Loss: 0.1841 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 13.716s | Local Loss: 0.2320 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.289s | Local Loss: 0.1334 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 12.877s | Local Loss: 0.1562 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.448s | Local Loss: 0.0345 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 12.099s | Local Loss: 0.0902 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 12.107s | Local Loss: 0.1623 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 12.105s | Local Loss: 0.1206 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 11.871s | Local Loss: 0.2984 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 12.923s | Local Loss: 0.1040 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 11.663s | Local Loss: 0.2927 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 13.049s | Local Loss: 0.5726 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 11.916s | Local Loss: 0.0845 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.255s | Local Loss: 0.1161 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.401s | Local Loss: 0.0644 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 11.846s | Local Loss: 0.0896 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 11.822s | Local Loss: 0.1892 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.358s | Local Loss: 0.0656 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.385s | Local Loss: 0.0938 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.512s | Local Loss: 0.0798 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 13.316s | Local Loss: 0.3974 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 11.729s | Local Loss: 0.1895 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 11.735s | Local Loss: 0.1693 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.407s | Local Loss: 0.0714 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.334s | Local Loss: 0.4317 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 11.817s | Local Loss: 0.4855 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.415s | Local Loss: 0.2730 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 11.930s | Local Loss: 0.3883 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 11.719s | Local Loss: 0.2456 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 12.170s | Local Loss: 0.0000 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.608s | Local Loss: 0.2358 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 12.103s | Local Loss: 0.2386 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 13.035s | Local Loss: 0.0374 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 11.800s | Local Loss: 0.0571 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 11.993s | Local Loss: 0.0654 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 12.093s | Local Loss: 0.4394 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 11.772s | Local Loss: 0.3768 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 11.816s | Local Loss: 0.3874 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 12.055s | Local Loss: 0.0413 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 20.961s | Local Loss: 0.0645 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 15.618s | Local Loss: 0.5536 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 15.713s | Local Loss: 0.1140 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 13.856s | Local Loss: 0.2240 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 13.452s | Local Loss: 0.0648 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 13.985s | Local Loss: 0.1170 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 14.893s | Local Loss: 0.1393 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 13.363s | Local Loss: 0.0450 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 13.108s | Local Loss: 0.0158 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 13.610s | Local Loss: 0.1640 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 13.531s | Local Loss: 0.3418 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 13.031s | Local Loss: 0.0794 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 13.549s | Local Loss: 0.3680 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 13.179s | Local Loss: 0.5090 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 13.100s | Local Loss: 0.0118 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 13.312s | Local Loss: 0.0535 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 13.096s | Local Loss: 0.3028 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 13.255s | Local Loss: 0.2550 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 12.961s | Local Loss: 0.1646 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 13.165s | Local Loss: 0.2824 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 13.053s | Local Loss: 0.4963 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 13.163s | Local Loss: 0.3667 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 13.330s | Local Loss: 0.0849 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 13.227s | Local Loss: 0.0353 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 13.355s | Local Loss: 0.4914 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 13.436s | Local Loss: 0.2099 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 13.076s | Local Loss: 0.0758 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 13.217s | Local Loss: 0.0702 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 13.426s | Local Loss: 0.0800 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 13.063s | Local Loss: 0.0266 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 13.025s | Local Loss: 0.1445 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 12.984s | Local Loss: 0.2919 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 12.915s | Local Loss: 0.0389 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 13.404s | Local Loss: 0.2716 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 12.838s | Local Loss: 0.0478 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 12.983s | Local Loss: 0.0563 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 13.341s | Local Loss: 0.0326 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 12.966s | Local Loss: 0.0121 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 13.140s | Local Loss: 0.0604 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 12.976s | Local Loss: 0.0194 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 12.811s | Local Loss: 0.1730 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 13.575s | Local Loss: 0.2529 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 12.861s | Local Loss: 0.0298 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 13.258s | Local Loss: 0.1289 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 13.372s | Local Loss: 0.2868 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 13.050s | Local Loss: 0.0520 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 13.297s | Local Loss: 0.0059 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 12.937s | Local Loss: 0.0321 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 12.814s | Local Loss: 0.3115 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 13.305s | Local Loss: 0.3307 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 20.148s | Local Loss: 0.4375 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 17.073s | Local Loss: 0.1142 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 18.121s | Local Loss: 0.0064 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 16.507s | Local Loss: 0.3527 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 17.202s | Local Loss: 0.2572 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 17.558s | Local Loss: 0.0501 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 17.204s | Local Loss: 0.0958 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 18.548s | Local Loss: 0.0532 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 13.615s | Local Loss: 0.0372 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 14.270s | Local Loss: 0.0916 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 13.519s | Local Loss: 0.3418 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 13.783s | Local Loss: 0.0255 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 13.501s | Local Loss: 0.2127 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 13.361s | Local Loss: 0.3943 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 13.628s | Local Loss: 0.1068 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 13.551s | Local Loss: 0.0141 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 13.464s | Local Loss: 0.0395 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 13.770s | Local Loss: 0.1798 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 14.392s | Local Loss: 0.0749 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 13.553s | Local Loss: 0.2963 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 13.728s | Local Loss: 0.1589 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 13.792s | Local Loss: 0.0356 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 13.222s | Local Loss: 0.3355 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 13.542s | Local Loss: 0.2007 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 13.430s | Local Loss: 0.0332 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 14.295s | Local Loss: 0.4145 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 13.710s | Local Loss: 0.1222 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 13.382s | Local Loss: 0.0623 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 13.934s | Local Loss: 0.0166 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 13.787s | Local Loss: 0.0268 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 13.408s | Local Loss: 0.0484 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 13.738s | Local Loss: 0.1425 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 13.633s | Local Loss: 0.0595 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 14.033s | Local Loss: 0.0450 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 13.710s | Local Loss: 0.1783 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 13.166s | Local Loss: 0.0487 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 13.907s | Local Loss: 0.0637 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 13.625s | Local Loss: 0.0218 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 13.572s | Local Loss: 0.3372 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 13.822s | Local Loss: 0.1582 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 14.085s | Local Loss: 0.2763 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 13.947s | Local Loss: 0.0204 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 13.918s | Local Loss: 0.1284 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 13.271s | Local Loss: 0.0891 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 14.031s | Local Loss: 0.1088 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 13.543s | Local Loss: 0.0330 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 13.562s | Local Loss: 0.2502 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 13.460s | Local Loss: 0.4759 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 13.603s | Local Loss: 0.2053 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 13.797s | Local Loss: 0.0175 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 19.894s | Local Loss: 0.0827 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 17.128s | Local Loss: 0.2416 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 18.050s | Local Loss: 0.2557 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 16.755s | Local Loss: 0.0209 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 17.768s | Local Loss: 0.0000 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 16.645s | Local Loss: 0.0447 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 17.315s | Local Loss: 0.0181 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 17.445s | Local Loss: 0.0655 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 17.992s | Local Loss: 0.0940 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 16.898s | Local Loss: 0.4520 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 19.198s | Local Loss: 0.0497 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 13.481s | Local Loss: 0.0586 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 14.165s | Local Loss: 0.0174 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 13.752s | Local Loss: 0.3638 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 13.693s | Local Loss: 0.0189 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 13.737s | Local Loss: 0.0414 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 13.275s | Local Loss: 0.0219 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 13.846s | Local Loss: 0.0583 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 13.478s | Local Loss: 0.0443 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 13.369s | Local Loss: 0.0671 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 13.678s | Local Loss: 0.0962 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 13.306s | Local Loss: 0.0101 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 13.627s | Local Loss: 0.3393 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 13.291s | Local Loss: 0.0776 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 13.487s | Local Loss: 0.1015 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 13.855s | Local Loss: 0.1158 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 13.510s | Local Loss: 0.3241 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 13.133s | Local Loss: 0.0445 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 13.897s | Local Loss: 0.0271 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 13.758s | Local Loss: 0.0388 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 13.704s | Local Loss: 0.1375 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 13.646s | Local Loss: 0.0606 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 13.950s | Local Loss: 0.0266 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 13.700s | Local Loss: 0.0830 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 13.785s | Local Loss: 0.0296 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 13.477s | Local Loss: 0.1173 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 13.801s | Local Loss: 0.2445 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 13.604s | Local Loss: 0.0416 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 13.459s | Local Loss: 0.0452 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 13.828s | Local Loss: 0.0321 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 13.625s | Local Loss: 0.3027 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 13.519s | Local Loss: 0.0265 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 13.811s | Local Loss: 0.0396 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 13.641s | Local Loss: 0.0292 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 13.723s | Local Loss: 0.0331 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 13.300s | Local Loss: 0.1705 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 13.382s | Local Loss: 0.0288 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 13.986s | Local Loss: 0.3919 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 13.888s | Local Loss: 0.0316 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 13.318s | Local Loss: 0.0326 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 18.097s | Local Loss: 0.0271 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 15.461s | Local Loss: 0.0737 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 15.128s | Local Loss: 0.0695 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 15.516s | Local Loss: 0.1867 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 15.321s | Local Loss: 0.0434 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 15.160s | Local Loss: 0.0442 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 15.559s | Local Loss: 0.0223 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 15.496s | Local Loss: 0.0284 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 14.964s | Local Loss: 0.0294 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 15.003s | Local Loss: 0.0299 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 15.554s | Local Loss: 0.4831 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 15.397s | Local Loss: 0.0613 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 14.861s | Local Loss: 0.0253 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 15.281s | Local Loss: 0.0335 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 15.379s | Local Loss: 0.1229 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 15.334s | Local Loss: 0.0326 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 14.835s | Local Loss: 0.0320 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 16.575s | Local Loss: 0.0328 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 13.591s | Local Loss: 0.2905 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 13.840s | Local Loss: 0.0535 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 13.712s | Local Loss: 0.0652 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 13.767s | Local Loss: 0.0441 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 13.433s | Local Loss: 0.3092 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 13.672s | Local Loss: 0.0567 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 13.318s | Local Loss: 0.0145 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 13.760s | Local Loss: 0.0479 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 13.718s | Local Loss: 0.1978 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 13.578s | Local Loss: 0.0247 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 13.323s | Local Loss: 0.0132 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 13.560s | Local Loss: 0.1871 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 13.407s | Local Loss: 0.0050 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 13.575s | Local Loss: 0.1249 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 13.776s | Local Loss: 0.0358 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 13.748s | Local Loss: 0.3308 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 13.480s | Local Loss: 0.1081 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 13.713s | Local Loss: 0.0377 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 13.622s | Local Loss: 0.0291 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 13.658s | Local Loss: 0.3092 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 13.803s | Local Loss: 0.0288 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 13.508s | Local Loss: 0.0426 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 13.470s | Local Loss: 0.2535 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 14.016s | Local Loss: 0.0083 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 13.744s | Local Loss: 0.1324 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 13.438s | Local Loss: 0.1219 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 13.871s | Local Loss: 0.0332 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 13.692s | Local Loss: 0.0312 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 13.645s | Local Loss: 0.0380 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 13.326s | Local Loss: 0.0889 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 13.616s | Local Loss: 0.0546 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 13.611s | Local Loss: 0.0915 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 20.195s | Local Loss: 0.0176 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 17.299s | Local Loss: 0.0260 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 17.952s | Local Loss: 0.0738 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 17.311s | Local Loss: 0.0337 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 17.527s | Local Loss: 0.0272 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 18.571s | Local Loss: 0.0365 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 14.408s | Local Loss: 0.0089 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 13.776s | Local Loss: 0.0480 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 13.605s | Local Loss: 0.3921 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 14.063s | Local Loss: 0.4532 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 13.313s | Local Loss: 0.0847 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 13.834s | Local Loss: 0.0580 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 13.694s | Local Loss: 0.1792 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 14.004s | Local Loss: 0.0505 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 13.757s | Local Loss: 0.0234 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 13.555s | Local Loss: 0.0415 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 13.299s | Local Loss: 0.0332 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 14.759s | Local Loss: 0.0151 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 13.397s | Local Loss: 0.0620 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 13.300s | Local Loss: 0.0000 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 13.575s | Local Loss: 0.0422 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 13.526s | Local Loss: 0.0154 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 17.064s | Local Loss: 0.0179 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 20.910s | Local Loss: 0.0568 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 20.407s | Local Loss: 0.0115 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 20.716s | Local Loss: 0.0655 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 20.601s | Local Loss: 0.2666 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 21.180s | Local Loss: 0.0315 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 20.821s | Local Loss: 0.0583 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 20.416s | Local Loss: 0.0394 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 21.061s | Local Loss: 0.2064 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 20.312s | Local Loss: 0.0573 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 21.662s | Local Loss: 0.0349 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 20.315s | Local Loss: 0.1546 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 20.978s | Local Loss: 0.0218 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 20.708s | Local Loss: 0.4329 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 21.663s | Local Loss: 0.0326 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 20.170s | Local Loss: 0.0261 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 22.455s | Local Loss: 0.0166 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 20.983s | Local Loss: 0.1337 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 25.160s | Local Loss: 0.2667 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 20.121s | Local Loss: 0.0175 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 21.500s | Local Loss: 0.0474 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 20.974s | Local Loss: 0.0470 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 21.522s | Local Loss: 0.1099 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 16.195s | Local Loss: 0.0237 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 13.298s | Local Loss: 0.0236 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 13.580s | Local Loss: 0.0160 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 13.842s | Local Loss: 0.1308 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 13.619s | Local Loss: 0.0153 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 18.219s | Local Loss: 0.2429 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 15.634s | Local Loss: 0.0444 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 15.603s | Local Loss: 0.0137 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 15.339s | Local Loss: 0.0716 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 14.890s | Local Loss: 0.0292 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 15.627s | Local Loss: 0.0235 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 15.275s | Local Loss: 0.0301 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 15.429s | Local Loss: 0.2578 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 15.157s | Local Loss: 0.1247 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 15.171s | Local Loss: 0.0169 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 15.999s | Local Loss: 0.0246 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 13.794s | Local Loss: 0.1241 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 13.624s | Local Loss: 0.0159 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 13.505s | Local Loss: 0.0460 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 13.361s | Local Loss: 0.0668 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 13.573s | Local Loss: 0.0462 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 14.012s | Local Loss: 0.0956 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 13.556s | Local Loss: 0.0222 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 13.429s | Local Loss: 0.0217 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 13.840s | Local Loss: 0.0488 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 13.649s | Local Loss: 0.0154 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 14.279s | Local Loss: 0.1457 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 14.663s | Local Loss: 0.0167 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 14.402s | Local Loss: 0.0222 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 14.262s | Local Loss: 0.0113 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 14.538s | Local Loss: 0.0650 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 14.391s | Local Loss: 0.0243 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 13.802s | Local Loss: 0.1397 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 14.031s | Local Loss: 0.0156 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 13.679s | Local Loss: 0.0043 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 14.066s | Local Loss: 0.0112 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 13.744s | Local Loss: 0.0712 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 13.986s | Local Loss: 0.0467 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 13.880s | Local Loss: 0.0160 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 13.821s | Local Loss: 0.0159 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 13.641s | Local Loss: 0.1002 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 14.206s | Local Loss: 0.0128 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 13.558s | Local Loss: 0.3737 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 13.620s | Local Loss: 0.4079 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 14.255s | Local Loss: 0.0274 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 13.337s | Local Loss: 0.0305 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 13.555s | Local Loss: 0.0831 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 13.517s | Local Loss: 0.1046 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 13.890s | Local Loss: 0.0878 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 14.155s | Local Loss: 0.2924 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 14.236s | Local Loss: 0.0264 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 13.776s | Local Loss: 0.0248 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 14.083s | Local Loss: 0.0265 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 13.586s | Local Loss: 0.0423 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 13.753s | Local Loss: 0.0178 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 20.155s | Local Loss: 0.0311 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 17.801s | Local Loss: 0.0442 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 16.971s | Local Loss: 0.0159 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 18.112s | Local Loss: 0.0223 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 17.144s | Local Loss: 0.0959 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 19.352s | Local Loss: 0.0371 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 17.498s | Local Loss: 0.0821 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 18.384s | Local Loss: 0.0207 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 17.542s | Local Loss: 0.0169 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 17.808s | Local Loss: 0.0219 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 18.202s | Local Loss: 0.0170 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 18.502s | Local Loss: 0.0272 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 17.097s | Local Loss: 0.0155 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 25.353s | Local Loss: 0.0222 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 20.306s | Local Loss: 0.0102 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 18.954s | Local Loss: 0.0195 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 14.339s | Local Loss: 0.2457 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 13.621s | Local Loss: 0.0903 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 13.162s | Local Loss: 0.0179 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 13.343s | Local Loss: 0.0243 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 13.400s | Local Loss: 0.0308 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 12.847s | Local Loss: 0.0444 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 13.349s | Local Loss: 0.0190 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 13.015s | Local Loss: 0.0333 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 13.079s | Local Loss: 0.0172 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 13.485s | Local Loss: 0.0542 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 13.149s | Local Loss: 0.0154 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 13.381s | Local Loss: 0.0403 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 13.457s | Local Loss: 0.0304 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 12.916s | Local Loss: 0.0200 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 13.197s | Local Loss: 0.0127 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 13.130s | Local Loss: 0.0578 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 13.397s | Local Loss: 0.0524 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 12.934s | Local Loss: 0.0242 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 13.037s | Local Loss: 0.0551 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 13.244s | Local Loss: 0.0061 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 12.761s | Local Loss: 0.0368 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 13.290s | Local Loss: 0.0079 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 13.009s | Local Loss: 0.0112 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 13.131s | Local Loss: 0.0072 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 13.156s | Local Loss: 0.0914 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 13.819s | Local Loss: 0.0231 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 16.308s | Local Loss: 0.0124 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 14.227s | Local Loss: 0.1387 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 14.260s | Local Loss: 0.0176 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 13.476s | Local Loss: 0.0450 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 14.247s | Local Loss: 0.0145 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_5.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_5.log
new file mode 100644
index 0000000000000000000000000000000000000000..deb10dc1ddf1adf7491c1832577dd8fed673aa7a
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_5.log
@@ -0,0 +1,900 @@
+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 17.588s | Local Loss: 0.1231 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 14.178s | Local Loss: 0.0980 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 13.411s | Local Loss: 0.1866 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 12.918s | Local Loss: 0.1359 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 12.026s | Local Loss: 0.0776 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 14.586s | Local Loss: 0.1875 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 22.872s | Local Loss: 0.2569 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 13.685s | Local Loss: 0.0994 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 14.097s | Local Loss: 0.1376 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 13.072s | Local Loss: 0.6006 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 13.706s | Local Loss: 0.0821 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 14.082s | Local Loss: 0.0312 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 14.769s | Local Loss: 0.0702 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 14.850s | Local Loss: 0.1891 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 13.035s | Local Loss: 0.3599 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 12.552s | Local Loss: 0.0502 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 13.485s | Local Loss: 0.0768 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 14.300s | Local Loss: 0.1155 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 13.372s | Local Loss: 0.1999 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 13.908s | Local Loss: 0.2279 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 12.264s | Local Loss: 0.1125 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 13.430s | Local Loss: 0.2152 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 13.201s | Local Loss: 0.1609 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 11.919s | Local Loss: 0.0553 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 12.511s | Local Loss: 0.0591 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 12.172s | Local Loss: 0.1099 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 11.886s | Local Loss: 0.2111 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 13.524s | Local Loss: 0.3294 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 11.988s | Local Loss: 0.0639 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 13.171s | Local Loss: 0.0609 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 12.622s | Local Loss: 0.4588 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 11.980s | Local Loss: 0.0598 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 12.503s | Local Loss: 0.1181 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 13.686s | Local Loss: 0.1038 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 12.563s | Local Loss: 0.1239 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 12.512s | Local Loss: 0.0618 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 13.482s | Local Loss: 0.0599 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 12.117s | Local Loss: 0.4217 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 11.915s | Local Loss: 0.2552 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 12.864s | Local Loss: 0.1348 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 13.160s | Local Loss: 0.2789 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 13.091s | Local Loss: 0.3678 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 13.422s | Local Loss: 0.2461 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 12.708s | Local Loss: 0.4555 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 12.331s | Local Loss: 0.0967 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 12.390s | Local Loss: 0.1197 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 12.527s | Local Loss: 0.2829 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 12.493s | Local Loss: 0.1890 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 12.955s | Local Loss: 0.3733 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 12.501s | Local Loss: 0.0409 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 20.698s | Local Loss: 0.0952 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 17.221s | Local Loss: 0.1064 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 16.694s | Local Loss: 0.0777 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 17.275s | Local Loss: 0.2567 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 13.892s | Local Loss: 0.1143 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 13.304s | Local Loss: 0.0407 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 13.289s | Local Loss: 0.0000 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 13.903s | Local Loss: 0.3106 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 12.867s | Local Loss: 0.1666 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 13.175s | Local Loss: 0.1270 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 13.928s | Local Loss: 0.0940 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 14.852s | Local Loss: 0.2304 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 14.374s | Local Loss: 0.0066 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 13.577s | Local Loss: 0.0359 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 14.955s | Local Loss: 0.3853 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 14.430s | Local Loss: 0.0508 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 15.250s | Local Loss: 0.0387 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 13.412s | Local Loss: 0.3993 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 14.044s | Local Loss: 0.0637 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 13.921s | Local Loss: 0.0479 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 13.609s | Local Loss: 0.0507 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 13.369s | Local Loss: 0.1672 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 13.258s | Local Loss: 0.1922 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 14.019s | Local Loss: 0.3080 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 13.396s | Local Loss: 0.2787 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 14.513s | Local Loss: 0.3023 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 13.447s | Local Loss: 0.5263 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 13.633s | Local Loss: 0.4905 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 13.732s | Local Loss: 0.0819 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 13.124s | Local Loss: 0.1187 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 13.372s | Local Loss: 0.5427 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 13.414s | Local Loss: 0.0179 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 12.871s | Local Loss: 0.0673 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 13.975s | Local Loss: 0.3096 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 13.353s | Local Loss: 0.3627 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 12.793s | Local Loss: 0.0446 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 13.756s | Local Loss: 0.0788 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 13.218s | Local Loss: 0.2831 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 13.490s | Local Loss: 0.0306 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 13.124s | Local Loss: 0.2581 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 13.494s | Local Loss: 0.0973 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 13.451s | Local Loss: 0.5370 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 13.285s | Local Loss: 0.1231 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 13.832s | Local Loss: 0.0941 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 13.150s | Local Loss: 0.0266 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 13.114s | Local Loss: 0.3184 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 13.062s | Local Loss: 0.1831 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 13.870s | Local Loss: 0.0792 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 12.979s | Local Loss: 0.0398 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 13.302s | Local Loss: 0.2500 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 20.318s | Local Loss: 0.0964 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 16.864s | Local Loss: 0.3656 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 14.612s | Local Loss: 0.0702 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 14.683s | Local Loss: 0.1358 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 14.859s | Local Loss: 0.0632 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 16.002s | Local Loss: 0.2235 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 15.877s | Local Loss: 0.1963 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 14.508s | Local Loss: 0.3203 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 14.616s | Local Loss: 0.0269 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 16.061s | Local Loss: 0.3205 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 14.815s | Local Loss: 0.0322 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 15.960s | Local Loss: 0.0299 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 14.002s | Local Loss: 0.0412 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 13.179s | Local Loss: 0.1055 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 13.717s | Local Loss: 0.0221 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 13.416s | Local Loss: 0.1156 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 13.901s | Local Loss: 0.2074 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 13.555s | Local Loss: 0.4227 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 13.348s | Local Loss: 0.2486 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 13.603s | Local Loss: 0.0557 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 13.196s | Local Loss: 0.3823 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 13.632s | Local Loss: 0.0170 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 13.481s | Local Loss: 0.1383 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 13.410s | Local Loss: 0.0276 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 14.282s | Local Loss: 0.0215 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 13.103s | Local Loss: 0.2991 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 13.579s | Local Loss: 0.4728 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 13.412s | Local Loss: 0.0395 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 12.990s | Local Loss: 0.0493 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 14.493s | Local Loss: 0.2648 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 13.239s | Local Loss: 0.4788 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 13.737s | Local Loss: 0.3336 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 13.323s | Local Loss: 0.0373 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 13.110s | Local Loss: 0.0731 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 13.972s | Local Loss: 0.3273 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 13.959s | Local Loss: 0.0677 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 13.579s | Local Loss: 0.4036 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 14.383s | Local Loss: 0.1035 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 13.550s | Local Loss: 0.0602 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 14.396s | Local Loss: 0.0324 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 14.160s | Local Loss: 0.0500 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 14.351s | Local Loss: 0.0445 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 13.308s | Local Loss: 0.0457 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 13.511s | Local Loss: 0.0348 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 13.527s | Local Loss: 0.0313 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 13.734s | Local Loss: 0.0726 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 13.320s | Local Loss: 0.0588 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 13.649s | Local Loss: 0.3038 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 14.059s | Local Loss: 0.0397 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 13.439s | Local Loss: 0.0484 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 21.665s | Local Loss: 0.0353 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 16.558s | Local Loss: 0.0486 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 17.516s | Local Loss: 0.2169 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 17.891s | Local Loss: 0.4144 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 18.080s | Local Loss: 0.3931 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 16.995s | Local Loss: 0.0374 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 17.117s | Local Loss: 0.2482 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 18.142s | Local Loss: 0.0529 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 17.669s | Local Loss: 0.0414 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 16.845s | Local Loss: 0.0684 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 16.590s | Local Loss: 0.0314 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 16.346s | Local Loss: 0.0169 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 17.434s | Local Loss: 0.0268 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 17.362s | Local Loss: 0.3724 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 16.949s | Local Loss: 0.1466 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 16.552s | Local Loss: 0.0214 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 16.962s | Local Loss: 0.0766 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 18.105s | Local Loss: 0.0313 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 14.388s | Local Loss: 0.0109 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 13.619s | Local Loss: 0.0501 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 14.573s | Local Loss: 0.0566 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 13.981s | Local Loss: 0.0715 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 13.184s | Local Loss: 0.0762 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 13.905s | Local Loss: 0.3165 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 14.162s | Local Loss: 0.0800 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 13.381s | Local Loss: 0.0206 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 13.545s | Local Loss: 0.0632 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 13.563s | Local Loss: 0.1752 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 14.969s | Local Loss: 0.1451 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 15.678s | Local Loss: 0.0685 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 13.969s | Local Loss: 0.0349 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 14.349s | Local Loss: 0.0084 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 13.969s | Local Loss: 0.0538 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 13.872s | Local Loss: 0.0116 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 13.709s | Local Loss: 0.1614 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 13.904s | Local Loss: 0.0363 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 13.636s | Local Loss: 0.0471 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 13.794s | Local Loss: 0.1295 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 13.945s | Local Loss: 0.2554 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 13.663s | Local Loss: 0.0313 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 13.671s | Local Loss: 0.0619 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 13.631s | Local Loss: 0.0403 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 13.930s | Local Loss: 0.3732 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 16.613s | Local Loss: 0.0272 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 17.186s | Local Loss: 0.1813 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 16.217s | Local Loss: 0.0334 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 15.565s | Local Loss: 0.0523 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 15.987s | Local Loss: 0.0109 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 13.752s | Local Loss: 0.0288 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 14.442s | Local Loss: 0.0959 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 22.029s | Local Loss: 0.0242 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 17.430s | Local Loss: 0.0474 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 16.382s | Local Loss: 0.0192 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 18.271s | Local Loss: 0.3521 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 14.195s | Local Loss: 0.0346 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 13.817s | Local Loss: 0.0209 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 13.422s | Local Loss: 0.0822 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 14.719s | Local Loss: 0.4867 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 13.740s | Local Loss: 0.0931 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 13.345s | Local Loss: 0.0252 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 13.662s | Local Loss: 0.2621 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 22.661s | Local Loss: 0.1258 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 20.786s | Local Loss: 0.0192 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 14.312s | Local Loss: 0.0818 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 14.075s | Local Loss: 0.0088 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 16.805s | Local Loss: 0.2996 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 14.301s | Local Loss: 0.0210 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 15.192s | Local Loss: 0.0345 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 13.839s | Local Loss: 0.0337 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 15.398s | Local Loss: 0.0362 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 13.437s | Local Loss: 0.0206 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 14.049s | Local Loss: 0.0387 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 13.972s | Local Loss: 0.0000 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 14.084s | Local Loss: 0.0776 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 14.974s | Local Loss: 0.0206 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 13.955s | Local Loss: 0.0222 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 13.634s | Local Loss: 0.2884 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 13.887s | Local Loss: 0.1754 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 14.338s | Local Loss: 0.4330 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 14.639s | Local Loss: 0.0440 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 13.933s | Local Loss: 0.1938 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 14.488s | Local Loss: 0.0222 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 13.588s | Local Loss: 0.0476 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 14.156s | Local Loss: 0.2209 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 13.695s | Local Loss: 0.1076 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 13.905s | Local Loss: 0.0236 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 14.432s | Local Loss: 0.2398 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 13.594s | Local Loss: 0.0230 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 14.548s | Local Loss: 0.0258 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 14.170s | Local Loss: 0.0311 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 13.926s | Local Loss: 0.0330 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 14.532s | Local Loss: 0.1129 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 13.540s | Local Loss: 0.0160 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 13.886s | Local Loss: 0.0335 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 14.505s | Local Loss: 0.3228 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 14.116s | Local Loss: 0.0076 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 14.870s | Local Loss: 0.0239 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 13.372s | Local Loss: 0.0383 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 14.785s | Local Loss: 0.0698 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 14.036s | Local Loss: 0.1104 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 21.674s | Local Loss: 0.0844 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 15.791s | Local Loss: 0.1449 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 14.816s | Local Loss: 0.0388 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 16.819s | Local Loss: 0.1531 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 14.607s | Local Loss: 0.2966 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 15.374s | Local Loss: 0.0252 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 14.861s | Local Loss: 0.0288 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 15.944s | Local Loss: 0.3413 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 16.173s | Local Loss: 0.0456 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 13.344s | Local Loss: 0.0373 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 14.699s | Local Loss: 0.0191 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 13.230s | Local Loss: 0.0104 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 13.265s | Local Loss: 0.0895 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 14.677s | Local Loss: 0.0429 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 13.205s | Local Loss: 0.3340 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 14.488s | Local Loss: 0.0975 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 13.668s | Local Loss: 0.3129 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 13.145s | Local Loss: 0.0743 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 14.775s | Local Loss: 0.0452 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 13.273s | Local Loss: 0.0424 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 13.301s | Local Loss: 0.0200 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 14.102s | Local Loss: 0.4351 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 13.457s | Local Loss: 0.0289 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 14.180s | Local Loss: 0.0994 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 13.385s | Local Loss: 0.1281 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 13.371s | Local Loss: 0.0635 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 14.527s | Local Loss: 0.0396 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 13.433s | Local Loss: 0.0349 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 14.768s | Local Loss: 0.0317 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 14.068s | Local Loss: 0.0953 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 13.434s | Local Loss: 0.0507 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 15.257s | Local Loss: 0.0345 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 13.301s | Local Loss: 0.0207 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 13.832s | Local Loss: 0.0175 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 15.680s | Local Loss: 0.0046 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 12.878s | Local Loss: 0.0228 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 13.518s | Local Loss: 0.0386 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 14.055s | Local Loss: 0.1534 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 13.830s | Local Loss: 0.2223 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 13.928s | Local Loss: 0.0221 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 13.330s | Local Loss: 0.0298 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 14.146s | Local Loss: 0.0543 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 14.375s | Local Loss: 0.0110 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 13.876s | Local Loss: 0.3468 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 13.773s | Local Loss: 0.0214 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 13.578s | Local Loss: 0.0440 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 13.652s | Local Loss: 0.1239 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 14.447s | Local Loss: 0.0081 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 14.079s | Local Loss: 0.0412 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 14.121s | Local Loss: 0.0143 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 24.335s | Local Loss: 0.0383 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 18.845s | Local Loss: 0.0000 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 16.963s | Local Loss: 0.0426 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 19.043s | Local Loss: 0.0178 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 18.537s | Local Loss: 0.0252 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 20.501s | Local Loss: 0.0437 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 17.371s | Local Loss: 0.0755 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 18.976s | Local Loss: 0.1358 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 17.299s | Local Loss: 0.0394 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 18.356s | Local Loss: 0.0213 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 17.404s | Local Loss: 0.0112 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 17.179s | Local Loss: 0.0189 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 17.369s | Local Loss: 0.0219 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 17.067s | Local Loss: 0.0328 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 18.373s | Local Loss: 0.0100 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 13.396s | Local Loss: 0.0045 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 13.708s | Local Loss: 0.0277 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 14.216s | Local Loss: 0.0349 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 14.461s | Local Loss: 0.0230 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 13.981s | Local Loss: 0.0958 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 13.386s | Local Loss: 0.2244 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 13.498s | Local Loss: 0.0097 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 14.215s | Local Loss: 0.0286 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 16.693s | Local Loss: 0.0703 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 15.609s | Local Loss: 0.0095 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 15.235s | Local Loss: 0.0100 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 16.822s | Local Loss: 0.0309 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 14.653s | Local Loss: 0.0097 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 13.976s | Local Loss: 0.0092 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 14.719s | Local Loss: 0.0298 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 14.844s | Local Loss: 0.0166 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 14.939s | Local Loss: 0.1449 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 14.721s | Local Loss: 0.0142 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 13.537s | Local Loss: 0.0115 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 14.981s | Local Loss: 0.0301 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 13.742s | Local Loss: 0.1133 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 14.126s | Local Loss: 0.3370 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 14.535s | Local Loss: 0.0733 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 14.448s | Local Loss: 0.0085 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 16.293s | Local Loss: 0.0254 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 15.667s | Local Loss: 0.0196 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 15.231s | Local Loss: 0.0346 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 14.354s | Local Loss: 0.0271 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 14.151s | Local Loss: 0.0057 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 14.000s | Local Loss: 0.0211 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 13.590s | Local Loss: 0.0310 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 14.028s | Local Loss: 0.1604 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 13.693s | Local Loss: 0.0553 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 14.338s | Local Loss: 0.0164 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 13.637s | Local Loss: 0.0149 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 19.053s | Local Loss: 0.0185 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 16.643s | Local Loss: 0.0131 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 17.422s | Local Loss: 0.1068 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 16.650s | Local Loss: 0.0145 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 17.183s | Local Loss: 0.1493 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 16.622s | Local Loss: 0.2169 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 15.091s | Local Loss: 0.0176 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 16.504s | Local Loss: 0.0185 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 14.417s | Local Loss: 0.0090 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 19.366s | Local Loss: 0.0386 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 24.318s | Local Loss: 0.0375 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 17.103s | Local Loss: 0.0545 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 15.051s | Local Loss: 0.0595 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 15.072s | Local Loss: 0.0637 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 15.219s | Local Loss: 0.0327 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 15.014s | Local Loss: 0.0165 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 15.506s | Local Loss: 0.0214 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 15.479s | Local Loss: 0.2304 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 15.294s | Local Loss: 0.0847 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 13.673s | Local Loss: 0.0941 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 13.182s | Local Loss: 0.0335 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 13.104s | Local Loss: 0.0092 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 13.821s | Local Loss: 0.0686 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 14.237s | Local Loss: 0.0160 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 14.658s | Local Loss: 0.0105 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 14.178s | Local Loss: 0.0101 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 13.854s | Local Loss: 0.1062 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 13.429s | Local Loss: 0.0103 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 13.074s | Local Loss: 0.0098 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 14.974s | Local Loss: 0.0168 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 13.079s | Local Loss: 0.0354 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 13.692s | Local Loss: 0.0075 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 13.253s | Local Loss: 0.0145 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 13.238s | Local Loss: 0.2776 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 14.442s | Local Loss: 0.0179 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 13.176s | Local Loss: 0.0388 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 12.873s | Local Loss: 0.1525 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 14.380s | Local Loss: 0.0803 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 13.246s | Local Loss: 0.3409 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 13.471s | Local Loss: 0.1515 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 13.527s | Local Loss: 0.0423 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 13.378s | Local Loss: 0.0073 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 14.234s | Local Loss: 0.0266 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 13.314s | Local Loss: 0.0202 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 13.397s | Local Loss: 0.1515 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 13.947s | Local Loss: 0.0089 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 13.087s | Local Loss: 0.0116 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 14.379s | Local Loss: 0.1772 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 12.978s | Local Loss: 0.0080 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 13.334s | Local Loss: 0.0946 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 21.633s | Local Loss: 0.0121 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 17.349s | Local Loss: 0.0104 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 16.896s | Local Loss: 0.0979 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 17.353s | Local Loss: 0.0141 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 16.904s | Local Loss: 0.0223 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 17.690s | Local Loss: 0.0054 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 18.281s | Local Loss: 0.0262 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 14.569s | Local Loss: 0.3725 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 13.025s | Local Loss: 0.0143 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 14.189s | Local Loss: 0.0070 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 13.646s | Local Loss: 0.0115 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 13.397s | Local Loss: 0.0117 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 13.211s | Local Loss: 0.0196 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 13.986s | Local Loss: 0.0110 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 13.786s | Local Loss: 0.0363 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 13.224s | Local Loss: 0.0564 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 13.977s | Local Loss: 0.0125 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 13.643s | Local Loss: 0.0096 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 12.949s | Local Loss: 0.0306 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 14.356s | Local Loss: 0.0805 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 13.967s | Local Loss: 0.0165 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 13.210s | Local Loss: 0.0091 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 13.263s | Local Loss: 0.0151 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 12.882s | Local Loss: 0.1127 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 13.416s | Local Loss: 0.0079 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 13.682s | Local Loss: 0.0235 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 12.939s | Local Loss: 0.2612 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 13.191s | Local Loss: 0.0201 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 13.613s | Local Loss: 0.0125 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 13.810s | Local Loss: 0.0407 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 13.730s | Local Loss: 0.0000 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 13.733s | Local Loss: 0.0253 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 14.222s | Local Loss: 0.0676 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 13.523s | Local Loss: 0.0208 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 14.659s | Local Loss: 0.0197 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 13.204s | Local Loss: 0.0188 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 13.718s | Local Loss: 0.0321 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 13.717s | Local Loss: 0.1512 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 13.512s | Local Loss: 0.0144 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 13.875s | Local Loss: 0.0463 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 13.079s | Local Loss: 0.0173 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 14.082s | Local Loss: 0.0148 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 13.993s | Local Loss: 0.2778 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 13.159s | Local Loss: 0.2676 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 13.920s | Local Loss: 0.0331 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 13.360s | Local Loss: 0.0234 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 14.110s | Local Loss: 0.1138 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 13.498s | Local Loss: 0.1953 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 13.618s | Local Loss: 0.0119 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 14.559s | Local Loss: 0.0491 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 21.304s | Local Loss: 0.0108 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 17.330s | Local Loss: 0.0423 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 16.420s | Local Loss: 0.1205 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 17.097s | Local Loss: 0.0916 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 17.154s | Local Loss: 0.0582 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 16.727s | Local Loss: 0.0532 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 18.521s | Local Loss: 0.0087 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 18.665s | Local Loss: 0.2947 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 18.242s | Local Loss: 0.1030 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 17.308s | Local Loss: 0.0072 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 17.834s | Local Loss: 0.0063 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 18.328s | Local Loss: 0.0113 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 14.531s | Local Loss: 0.0093 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 13.591s | Local Loss: 0.0107 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 13.693s | Local Loss: 0.0100 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 14.169s | Local Loss: 0.0122 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 13.376s | Local Loss: 0.0142 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 13.662s | Local Loss: 0.0364 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 15.541s | Local Loss: 0.0301 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 13.231s | Local Loss: 0.0094 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 13.988s | Local Loss: 0.0059 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 14.016s | Local Loss: 0.0156 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 13.501s | Local Loss: 0.0740 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 14.276s | Local Loss: 0.0104 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 13.234s | Local Loss: 0.0965 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 13.968s | Local Loss: 0.0082 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 13.872s | Local Loss: 0.0030 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 14.657s | Local Loss: 0.0063 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 13.249s | Local Loss: 0.0082 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 14.357s | Local Loss: 0.0381 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 14.724s | Local Loss: 0.0186 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 14.083s | Local Loss: 0.0117 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 14.424s | Local Loss: 0.0134 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 14.559s | Local Loss: 0.0168 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 14.500s | Local Loss: 0.0144 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 13.814s | Local Loss: 0.3443 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 14.789s | Local Loss: 0.0146 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 14.461s | Local Loss: 0.0036 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 13.624s | Local Loss: 0.0334 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 14.394s | Local Loss: 0.0329 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 14.326s | Local Loss: 0.0176 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 13.584s | Local Loss: 0.0091 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 13.157s | Local Loss: 0.0449 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 15.738s | Local Loss: 0.0093 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 13.246s | Local Loss: 0.0348 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 13.741s | Local Loss: 0.0213 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 13.744s | Local Loss: 0.0052 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 13.546s | Local Loss: 0.0171 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 14.183s | Local Loss: 0.0216 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 13.432s | Local Loss: 0.1311 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 22:10:07] Logger closing.
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 14.578s | Local Loss: 0.1231 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 13.710s | Local Loss: 0.0980 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.202s | Local Loss: 0.1866 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.098s | Local Loss: 0.1359 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 12.732s | Local Loss: 0.0778 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 11.962s | Local Loss: 0.1876 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.018s | Local Loss: 0.2569 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 12.285s | Local Loss: 0.0994 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.568s | Local Loss: 0.1381 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 14.831s | Local Loss: 0.6030 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 13.990s | Local Loss: 0.0824 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.108s | Local Loss: 0.0304 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 13.522s | Local Loss: 0.0698 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.011s | Local Loss: 0.1895 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 12.695s | Local Loss: 0.3604 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.132s | Local Loss: 0.0496 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 11.770s | Local Loss: 0.0769 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 11.908s | Local Loss: 0.1154 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 11.927s | Local Loss: 0.1999 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 11.854s | Local Loss: 0.2279 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 12.861s | Local Loss: 0.1125 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 11.564s | Local Loss: 0.2155 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 12.812s | Local Loss: 0.1611 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 11.924s | Local Loss: 0.0549 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.118s | Local Loss: 0.0593 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.410s | Local Loss: 0.1107 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 11.810s | Local Loss: 0.2117 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 11.759s | Local Loss: 0.3301 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.166s | Local Loss: 0.0638 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.364s | Local Loss: 0.0619 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.387s | Local Loss: 0.4595 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 12.991s | Local Loss: 0.0600 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 11.518s | Local Loss: 0.1182 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 11.556s | Local Loss: 0.1041 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.169s | Local Loss: 0.1242 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.333s | Local Loss: 0.0623 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 11.548s | Local Loss: 0.0604 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.165s | Local Loss: 0.4225 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 11.839s | Local Loss: 0.2559 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 11.659s | Local Loss: 0.1352 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 11.988s | Local Loss: 0.2793 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.473s | Local Loss: 0.3691 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 11.768s | Local Loss: 0.2470 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 12.752s | Local Loss: 0.4560 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 11.599s | Local Loss: 0.0971 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 11.680s | Local Loss: 0.1205 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 11.753s | Local Loss: 0.2839 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 11.796s | Local Loss: 0.1897 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 11.657s | Local Loss: 0.3741 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 11.720s | Local Loss: 0.0387 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 20.715s | Local Loss: 0.0940 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 15.386s | Local Loss: 0.1012 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 15.451s | Local Loss: 0.0777 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 13.765s | Local Loss: 0.2577 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 13.188s | Local Loss: 0.1151 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 13.657s | Local Loss: 0.0386 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 14.574s | Local Loss: 0.0000 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 13.086s | Local Loss: 0.3119 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 12.941s | Local Loss: 0.1670 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 13.445s | Local Loss: 0.1275 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 13.380s | Local Loss: 0.0941 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 12.833s | Local Loss: 0.2313 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 13.383s | Local Loss: 0.0067 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 12.858s | Local Loss: 0.0363 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 12.932s | Local Loss: 0.3865 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 13.243s | Local Loss: 0.0515 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 12.761s | Local Loss: 0.0381 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 13.187s | Local Loss: 0.4008 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 12.630s | Local Loss: 0.0617 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 13.012s | Local Loss: 0.0477 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 12.827s | Local Loss: 0.0526 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 13.015s | Local Loss: 0.1685 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 13.045s | Local Loss: 0.1934 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 13.052s | Local Loss: 0.3099 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 13.098s | Local Loss: 0.2806 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 13.212s | Local Loss: 0.3050 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 12.957s | Local Loss: 0.5261 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 13.014s | Local Loss: 0.4909 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 13.330s | Local Loss: 0.0822 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 12.945s | Local Loss: 0.1200 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 12.814s | Local Loss: 0.5431 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 12.729s | Local Loss: 0.0244 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 12.688s | Local Loss: 0.0717 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 13.106s | Local Loss: 0.3114 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 12.918s | Local Loss: 0.3641 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 12.698s | Local Loss: 0.0453 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 13.214s | Local Loss: 0.0793 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 12.736s | Local Loss: 0.2845 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 12.910s | Local Loss: 0.0298 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 12.840s | Local Loss: 0.2599 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 12.610s | Local Loss: 0.0980 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 13.355s | Local Loss: 0.5371 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 12.855s | Local Loss: 0.1248 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 12.999s | Local Loss: 0.0954 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 13.200s | Local Loss: 0.0253 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 12.798s | Local Loss: 0.3201 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 13.091s | Local Loss: 0.1865 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 12.644s | Local Loss: 0.0820 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 12.527s | Local Loss: 0.0384 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 12.921s | Local Loss: 0.2544 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 19.886s | Local Loss: 0.0970 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 16.761s | Local Loss: 0.3663 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 17.925s | Local Loss: 0.0724 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 16.289s | Local Loss: 0.1367 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 16.872s | Local Loss: 0.0662 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 17.429s | Local Loss: 0.2275 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 17.044s | Local Loss: 0.1976 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 18.213s | Local Loss: 0.3205 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 13.384s | Local Loss: 0.0272 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 14.198s | Local Loss: 0.3225 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 13.307s | Local Loss: 0.0314 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 13.690s | Local Loss: 0.0290 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 13.375s | Local Loss: 0.0394 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 12.976s | Local Loss: 0.1065 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 13.590s | Local Loss: 0.0219 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 13.259s | Local Loss: 0.1168 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 13.397s | Local Loss: 0.2098 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 13.494s | Local Loss: 0.4246 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 14.015s | Local Loss: 0.2500 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 13.363s | Local Loss: 0.0548 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 13.573s | Local Loss: 0.3844 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 13.631s | Local Loss: 0.0163 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 13.066s | Local Loss: 0.1413 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 13.013s | Local Loss: 0.0285 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 13.112s | Local Loss: 0.0199 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 13.988s | Local Loss: 0.2991 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 13.528s | Local Loss: 0.4736 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 13.126s | Local Loss: 0.0404 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 13.771s | Local Loss: 0.0553 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 13.599s | Local Loss: 0.2672 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 13.169s | Local Loss: 0.4794 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 13.524s | Local Loss: 0.3372 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 13.496s | Local Loss: 0.0375 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 13.724s | Local Loss: 0.0735 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 13.469s | Local Loss: 0.3285 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 12.875s | Local Loss: 0.0686 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 13.842s | Local Loss: 0.4054 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 13.285s | Local Loss: 0.1045 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 13.246s | Local Loss: 0.0609 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 13.693s | Local Loss: 0.0331 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 13.658s | Local Loss: 0.0505 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 13.868s | Local Loss: 0.0435 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 13.795s | Local Loss: 0.0457 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 12.860s | Local Loss: 0.0351 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 13.864s | Local Loss: 0.0321 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 13.144s | Local Loss: 0.0736 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 13.384s | Local Loss: 0.0596 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 13.038s | Local Loss: 0.3042 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 13.319s | Local Loss: 0.0398 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 13.625s | Local Loss: 0.0458 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 19.534s | Local Loss: 0.0355 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 16.781s | Local Loss: 0.0485 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 17.800s | Local Loss: 0.2189 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 16.500s | Local Loss: 0.4171 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 17.727s | Local Loss: 0.3931 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 16.309s | Local Loss: 0.0378 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 17.147s | Local Loss: 0.2493 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 17.193s | Local Loss: 0.0531 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 17.911s | Local Loss: 0.0419 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 16.511s | Local Loss: 0.0695 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 19.174s | Local Loss: 0.0307 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 13.150s | Local Loss: 0.0178 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 14.089s | Local Loss: 0.0268 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 13.511s | Local Loss: 0.3722 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 13.564s | Local Loss: 0.1477 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 13.505s | Local Loss: 0.0204 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 12.938s | Local Loss: 0.0778 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 13.665s | Local Loss: 0.0338 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 13.300s | Local Loss: 0.0089 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 13.121s | Local Loss: 0.0516 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 13.575s | Local Loss: 0.0579 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 13.130s | Local Loss: 0.0721 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 13.397s | Local Loss: 0.0782 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 13.056s | Local Loss: 0.3166 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 13.333s | Local Loss: 0.0812 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 13.665s | Local Loss: 0.0218 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 13.309s | Local Loss: 0.0640 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 13.082s | Local Loss: 0.1777 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 13.725s | Local Loss: 0.1470 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 13.470s | Local Loss: 0.0692 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 13.474s | Local Loss: 0.0366 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 13.539s | Local Loss: 0.0086 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 13.750s | Local Loss: 0.0544 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 13.566s | Local Loss: 0.0110 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 13.585s | Local Loss: 0.1635 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 13.347s | Local Loss: 0.0339 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 13.549s | Local Loss: 0.0483 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 13.401s | Local Loss: 0.1320 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 13.241s | Local Loss: 0.2586 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 13.517s | Local Loss: 0.0318 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 13.604s | Local Loss: 0.0625 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 13.281s | Local Loss: 0.0410 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 13.705s | Local Loss: 0.3746 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 13.409s | Local Loss: 0.0254 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 13.524s | Local Loss: 0.1835 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 13.119s | Local Loss: 0.0350 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 13.271s | Local Loss: 0.0526 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 13.648s | Local Loss: 0.0087 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 13.781s | Local Loss: 0.0213 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 13.140s | Local Loss: 0.0971 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 17.788s | Local Loss: 0.0218 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 15.246s | Local Loss: 0.0479 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 14.994s | Local Loss: 0.0173 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 15.180s | Local Loss: 0.3539 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 15.401s | Local Loss: 0.0350 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 14.830s | Local Loss: 0.0182 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 15.519s | Local Loss: 0.0817 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 15.223s | Local Loss: 0.4861 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 14.793s | Local Loss: 0.0935 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 14.835s | Local Loss: 0.0290 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 15.345s | Local Loss: 0.2630 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 15.209s | Local Loss: 0.1257 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 14.744s | Local Loss: 0.0179 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 15.266s | Local Loss: 0.0831 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 15.207s | Local Loss: 0.0107 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 15.060s | Local Loss: 0.3014 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 14.655s | Local Loss: 0.0154 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 16.346s | Local Loss: 0.0357 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 13.567s | Local Loss: 0.0340 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 13.590s | Local Loss: 0.0375 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 13.372s | Local Loss: 0.0248 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 13.528s | Local Loss: 0.0394 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 13.220s | Local Loss: 0.0000 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 13.726s | Local Loss: 0.0788 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 13.200s | Local Loss: 0.0185 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 13.562s | Local Loss: 0.0220 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 12.962s | Local Loss: 0.2896 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 13.322s | Local Loss: 0.1772 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 13.303s | Local Loss: 0.4329 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 13.330s | Local Loss: 0.0448 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 12.987s | Local Loss: 0.1959 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 13.392s | Local Loss: 0.0129 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 13.498s | Local Loss: 0.0488 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 13.477s | Local Loss: 0.2291 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 13.326s | Local Loss: 0.1112 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 13.576s | Local Loss: 0.0243 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 13.277s | Local Loss: 0.2424 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 13.643s | Local Loss: 0.0227 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 13.667s | Local Loss: 0.0315 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 13.257s | Local Loss: 0.0328 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 13.220s | Local Loss: 0.0347 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 13.658s | Local Loss: 0.1158 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 13.411s | Local Loss: 0.0168 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 13.094s | Local Loss: 0.0349 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 13.712s | Local Loss: 0.3260 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 13.590s | Local Loss: 0.0082 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 13.416s | Local Loss: 0.0227 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 13.654s | Local Loss: 0.0396 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 13.527s | Local Loss: 0.0719 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 13.351s | Local Loss: 0.1127 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 19.894s | Local Loss: 0.0863 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 17.232s | Local Loss: 0.1474 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 17.646s | Local Loss: 0.0415 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 17.202s | Local Loss: 0.1565 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 17.379s | Local Loss: 0.3032 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 18.479s | Local Loss: 0.0262 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 14.303s | Local Loss: 0.0300 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 13.810s | Local Loss: 0.3441 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 13.516s | Local Loss: 0.0476 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 13.832s | Local Loss: 0.0229 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 13.300s | Local Loss: 0.0204 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 13.586s | Local Loss: 0.0121 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 13.496s | Local Loss: 0.0916 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 13.981s | Local Loss: 0.0443 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 13.493s | Local Loss: 0.3379 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 13.309s | Local Loss: 0.0999 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 13.173s | Local Loss: 0.3129 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 14.594s | Local Loss: 0.0748 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 13.135s | Local Loss: 0.0457 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 13.238s | Local Loss: 0.0425 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 13.473s | Local Loss: 0.0212 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 13.465s | Local Loss: 0.4365 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 16.715s | Local Loss: 0.0288 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 20.762s | Local Loss: 0.1016 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 20.209s | Local Loss: 0.1283 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 20.521s | Local Loss: 0.0645 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 20.332s | Local Loss: 0.0391 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 20.970s | Local Loss: 0.0356 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 20.526s | Local Loss: 0.0321 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 20.201s | Local Loss: 0.0963 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 20.805s | Local Loss: 0.0507 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 20.238s | Local Loss: 0.0347 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 21.416s | Local Loss: 0.0210 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 20.200s | Local Loss: 0.0244 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 20.808s | Local Loss: 0.0060 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 20.481s | Local Loss: 0.0258 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 21.505s | Local Loss: 0.0379 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 20.009s | Local Loss: 0.1535 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 22.221s | Local Loss: 0.2218 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 20.755s | Local Loss: 0.0248 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 25.004s | Local Loss: 0.0305 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 19.939s | Local Loss: 0.0604 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 21.198s | Local Loss: 0.0107 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 20.800s | Local Loss: 0.3477 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 21.226s | Local Loss: 0.0223 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 16.025s | Local Loss: 0.0445 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 13.114s | Local Loss: 0.1270 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 13.482s | Local Loss: 0.0075 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 13.639s | Local Loss: 0.0411 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 13.230s | Local Loss: 0.0140 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 17.896s | Local Loss: 0.0412 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 15.512s | Local Loss: 0.0000 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 15.294s | Local Loss: 0.0460 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 15.124s | Local Loss: 0.0203 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 14.649s | Local Loss: 0.0254 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 15.516s | Local Loss: 0.0430 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 15.121s | Local Loss: 0.0754 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 15.186s | Local Loss: 0.1386 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 15.007s | Local Loss: 0.0396 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 14.866s | Local Loss: 0.0215 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 15.997s | Local Loss: 0.0134 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 13.563s | Local Loss: 0.0187 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 13.331s | Local Loss: 0.0259 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 13.361s | Local Loss: 0.0332 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 13.031s | Local Loss: 0.0116 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 13.427s | Local Loss: 0.0042 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 13.940s | Local Loss: 0.0277 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 13.244s | Local Loss: 0.0359 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 13.390s | Local Loss: 0.0237 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 13.541s | Local Loss: 0.0955 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 13.473s | Local Loss: 0.2225 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 14.019s | Local Loss: 0.0094 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 14.355s | Local Loss: 0.0289 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 14.157s | Local Loss: 0.0688 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 14.199s | Local Loss: 0.0087 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 14.261s | Local Loss: 0.0096 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 14.219s | Local Loss: 0.0309 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 13.469s | Local Loss: 0.0122 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 13.833s | Local Loss: 0.0118 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 13.343s | Local Loss: 0.0284 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 13.739s | Local Loss: 0.0165 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 13.482s | Local Loss: 0.1417 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 13.820s | Local Loss: 0.0153 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 13.646s | Local Loss: 0.0151 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 13.673s | Local Loss: 0.0300 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 13.464s | Local Loss: 0.1090 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 14.156s | Local Loss: 0.3264 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 13.277s | Local Loss: 0.0731 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 13.377s | Local Loss: 0.0087 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 14.064s | Local Loss: 0.0306 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 13.289s | Local Loss: 0.0183 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 13.227s | Local Loss: 0.0341 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 13.211s | Local Loss: 0.0267 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 13.589s | Local Loss: 0.0063 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 13.682s | Local Loss: 0.0205 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 14.123s | Local Loss: 0.0197 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 13.595s | Local Loss: 0.1562 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 13.904s | Local Loss: 0.0543 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 13.516s | Local Loss: 0.0169 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 13.527s | Local Loss: 0.0157 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 19.909s | Local Loss: 0.0187 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 17.564s | Local Loss: 0.0139 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 16.683s | Local Loss: 0.1047 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 17.971s | Local Loss: 0.0137 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 16.947s | Local Loss: 0.1501 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 19.155s | Local Loss: 0.2182 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 17.174s | Local Loss: 0.0160 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 18.135s | Local Loss: 0.0185 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 17.187s | Local Loss: 0.0088 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 17.644s | Local Loss: 0.0395 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 18.001s | Local Loss: 0.0387 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 18.255s | Local Loss: 0.0551 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 16.834s | Local Loss: 0.0610 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 25.140s | Local Loss: 0.0657 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 20.261s | Local Loss: 0.0339 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 18.816s | Local Loss: 0.0182 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 14.167s | Local Loss: 0.0223 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 13.548s | Local Loss: 0.2333 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 12.890s | Local Loss: 0.0861 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 13.039s | Local Loss: 0.0960 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 13.161s | Local Loss: 0.0335 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 12.588s | Local Loss: 0.0130 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 13.220s | Local Loss: 0.0700 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 12.808s | Local Loss: 0.0138 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 12.918s | Local Loss: 0.0146 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 13.115s | Local Loss: 0.0103 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 12.848s | Local Loss: 0.1089 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 13.080s | Local Loss: 0.0111 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 13.214s | Local Loss: 0.0100 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 12.599s | Local Loss: 0.0181 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 13.051s | Local Loss: 0.0377 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 12.827s | Local Loss: 0.0077 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 13.046s | Local Loss: 0.0149 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 12.696s | Local Loss: 0.2785 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 12.758s | Local Loss: 0.0184 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 12.997s | Local Loss: 0.0393 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 12.444s | Local Loss: 0.1574 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 12.880s | Local Loss: 0.0817 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 12.866s | Local Loss: 0.3453 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 12.891s | Local Loss: 0.1567 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 12.910s | Local Loss: 0.0436 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 13.626s | Local Loss: 0.0076 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 16.167s | Local Loss: 0.0208 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 14.071s | Local Loss: 0.0200 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 13.970s | Local Loss: 0.1537 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 13.124s | Local Loss: 0.0081 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 14.129s | Local Loss: 0.0092 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_6.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_6.log
new file mode 100644
index 0000000000000000000000000000000000000000..5f10a2cd76b38ec06735d7e455b9b9c46ea7e2f4
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_6.log
@@ -0,0 +1,900 @@
+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 17.366s | Local Loss: 0.1352 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 14.465s | Local Loss: 0.5310 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 13.577s | Local Loss: 0.2672 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 12.901s | Local Loss: 0.2286 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 12.214s | Local Loss: 0.1746 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 14.653s | Local Loss: 0.2394 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 23.253s | Local Loss: 0.2720 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 13.923s | Local Loss: 0.2770 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 14.356s | Local Loss: 0.3338 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 13.192s | Local Loss: 0.1226 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 13.800s | Local Loss: 0.2409 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 13.972s | Local Loss: 0.2655 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 14.806s | Local Loss: 0.4094 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 14.961s | Local Loss: 0.2861 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 13.260s | Local Loss: 0.2147 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 12.649s | Local Loss: 0.1204 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 13.704s | Local Loss: 0.4730 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 14.199s | Local Loss: 0.3113 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 13.362s | Local Loss: 0.1175 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 13.740s | Local Loss: 0.0370 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 12.515s | Local Loss: 0.5709 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 13.489s | Local Loss: 0.3017 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 13.448s | Local Loss: 0.1257 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 11.899s | Local Loss: 0.3737 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 12.533s | Local Loss: 0.2669 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 12.273s | Local Loss: 0.1023 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 11.929s | Local Loss: 0.1804 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 13.496s | Local Loss: 0.0259 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 12.142s | Local Loss: 0.0616 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 13.358s | Local Loss: 0.0144 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 12.980s | Local Loss: 0.4898 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 12.222s | Local Loss: 0.2026 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 12.690s | Local Loss: 0.0583 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 13.890s | Local Loss: 0.3360 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 12.594s | Local Loss: 0.4114 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 12.537s | Local Loss: 0.0337 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 13.677s | Local Loss: 0.1993 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 12.134s | Local Loss: 0.1520 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 12.073s | Local Loss: 0.2087 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 12.921s | Local Loss: 0.3345 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 13.203s | Local Loss: 0.1238 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 13.190s | Local Loss: 0.0534 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 13.364s | Local Loss: 0.1250 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 12.761s | Local Loss: 0.2820 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 12.556s | Local Loss: 0.0346 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 12.501s | Local Loss: 0.1188 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 12.565s | Local Loss: 0.1562 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 12.574s | Local Loss: 0.2250 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 12.916s | Local Loss: 0.0345 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 12.693s | Local Loss: 0.2334 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 20.923s | Local Loss: 0.2385 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 17.214s | Local Loss: 0.0674 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 17.000s | Local Loss: 0.0401 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 17.405s | Local Loss: 0.0304 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 14.150s | Local Loss: 0.1251 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 13.470s | Local Loss: 0.0516 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 13.616s | Local Loss: 0.1520 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 13.945s | Local Loss: 0.1173 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 12.977s | Local Loss: 0.1486 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 13.258s | Local Loss: 0.0755 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 14.054s | Local Loss: 0.3327 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 14.884s | Local Loss: 0.0271 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 14.492s | Local Loss: 0.0687 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 13.886s | Local Loss: 0.2816 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 15.143s | Local Loss: 0.3500 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 14.185s | Local Loss: 0.1043 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 15.189s | Local Loss: 0.0526 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 13.628s | Local Loss: 0.0867 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 14.359s | Local Loss: 0.3295 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 14.015s | Local Loss: 0.0724 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 13.679s | Local Loss: 0.0187 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 13.437s | Local Loss: 0.3448 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 13.496s | Local Loss: 0.0644 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 14.183s | Local Loss: 0.0469 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 13.494s | Local Loss: 0.1001 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 14.607s | Local Loss: 0.3482 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 13.611s | Local Loss: 0.1108 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 13.620s | Local Loss: 0.3071 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 13.691s | Local Loss: 0.4377 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 13.167s | Local Loss: 0.2115 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 13.306s | Local Loss: 0.0198 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 13.450s | Local Loss: 0.1362 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 13.171s | Local Loss: 0.2615 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 14.021s | Local Loss: 0.0520 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 13.645s | Local Loss: 0.1398 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 12.920s | Local Loss: 0.0512 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 13.947s | Local Loss: 0.2953 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 13.425s | Local Loss: 0.1676 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 13.779s | Local Loss: 0.0424 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 13.253s | Local Loss: 0.0471 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 13.523s | Local Loss: 0.1527 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 13.676s | Local Loss: 0.4572 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 13.443s | Local Loss: 0.0480 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 14.031s | Local Loss: 0.0498 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 13.280s | Local Loss: 0.0376 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 13.374s | Local Loss: 0.0511 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 13.327s | Local Loss: 0.2677 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 14.121s | Local Loss: 0.0253 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 13.195s | Local Loss: 0.0162 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 13.520s | Local Loss: 0.1601 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 20.474s | Local Loss: 0.3295 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 16.888s | Local Loss: 0.4625 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 14.825s | Local Loss: 0.0000 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 14.919s | Local Loss: 0.2955 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 15.262s | Local Loss: 0.5352 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 16.191s | Local Loss: 0.0289 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 16.150s | Local Loss: 0.1006 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 14.791s | Local Loss: 0.4088 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 14.760s | Local Loss: 0.2951 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 16.416s | Local Loss: 0.3095 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 15.009s | Local Loss: 0.1410 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 16.122s | Local Loss: 0.1482 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 14.259s | Local Loss: 0.0881 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 13.391s | Local Loss: 0.0557 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 13.770s | Local Loss: 0.3296 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 13.456s | Local Loss: 0.1405 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 13.984s | Local Loss: 0.1857 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 13.659s | Local Loss: 0.0393 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 13.486s | Local Loss: 0.2051 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 13.657s | Local Loss: 0.0282 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 13.202s | Local Loss: 0.0557 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 13.557s | Local Loss: 0.0759 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 13.679s | Local Loss: 0.1986 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 13.680s | Local Loss: 0.0192 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 14.488s | Local Loss: 0.0122 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 13.166s | Local Loss: 0.0614 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 13.711s | Local Loss: 0.0147 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 13.711s | Local Loss: 0.0129 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 13.233s | Local Loss: 0.2129 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 14.497s | Local Loss: 0.3342 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 13.426s | Local Loss: 0.0617 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 13.957s | Local Loss: 0.2390 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 13.417s | Local Loss: 0.2644 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 13.209s | Local Loss: 0.0370 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 14.146s | Local Loss: 0.0492 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 14.213s | Local Loss: 0.1384 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 13.676s | Local Loss: 0.0190 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 14.653s | Local Loss: 0.2121 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 13.867s | Local Loss: 0.3378 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 14.500s | Local Loss: 0.2773 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 14.328s | Local Loss: 0.0413 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 14.613s | Local Loss: 0.0753 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 13.509s | Local Loss: 0.0994 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 13.808s | Local Loss: 0.1361 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 13.730s | Local Loss: 0.0209 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 13.975s | Local Loss: 0.0753 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 13.415s | Local Loss: 0.0161 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 13.987s | Local Loss: 0.4344 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 14.123s | Local Loss: 0.1424 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 13.608s | Local Loss: 0.1957 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 21.880s | Local Loss: 0.0917 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 16.583s | Local Loss: 0.3087 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 17.509s | Local Loss: 0.0542 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 17.962s | Local Loss: 0.0091 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 18.123s | Local Loss: 0.0938 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 16.933s | Local Loss: 0.0206 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 17.118s | Local Loss: 0.0919 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 18.115s | Local Loss: 0.1245 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 17.604s | Local Loss: 0.0602 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 16.836s | Local Loss: 0.0648 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 16.607s | Local Loss: 0.0695 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 16.339s | Local Loss: 0.0712 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 17.436s | Local Loss: 0.0386 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 17.286s | Local Loss: 0.0247 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 16.986s | Local Loss: 0.0472 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 16.628s | Local Loss: 0.0515 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 16.993s | Local Loss: 0.0476 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 18.120s | Local Loss: 0.0539 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 14.279s | Local Loss: 0.1695 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 13.511s | Local Loss: 0.4335 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 14.441s | Local Loss: 0.0385 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 14.231s | Local Loss: 0.0307 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 13.312s | Local Loss: 0.0759 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 14.195s | Local Loss: 0.0390 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 14.119s | Local Loss: 0.0475 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 13.436s | Local Loss: 0.4915 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 13.791s | Local Loss: 0.1073 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 13.644s | Local Loss: 0.0869 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 15.099s | Local Loss: 0.0445 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 16.053s | Local Loss: 0.4342 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 14.154s | Local Loss: 0.0502 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 14.497s | Local Loss: 0.0215 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 14.187s | Local Loss: 0.0385 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 14.041s | Local Loss: 0.0864 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 14.044s | Local Loss: 0.1459 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 14.268s | Local Loss: 0.1207 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 13.779s | Local Loss: 0.2828 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 13.931s | Local Loss: 0.2880 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 14.091s | Local Loss: 0.1079 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 13.719s | Local Loss: 0.0171 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 13.872s | Local Loss: 0.0387 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 13.688s | Local Loss: 0.0171 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 13.983s | Local Loss: 0.0591 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 16.700s | Local Loss: 0.4104 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 17.275s | Local Loss: 0.0182 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 16.354s | Local Loss: 0.0285 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 15.679s | Local Loss: 0.0397 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 16.112s | Local Loss: 0.0311 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 14.008s | Local Loss: 0.2266 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 14.369s | Local Loss: 0.0306 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 22.198s | Local Loss: 0.0406 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 17.552s | Local Loss: 0.4149 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 16.622s | Local Loss: 0.0095 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 18.348s | Local Loss: 0.0258 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 14.261s | Local Loss: 0.0797 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 13.989s | Local Loss: 0.3886 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 13.234s | Local Loss: 0.0345 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 14.552s | Local Loss: 0.0287 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 13.774s | Local Loss: 0.0264 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 13.435s | Local Loss: 0.0342 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 13.579s | Local Loss: 0.0235 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 22.647s | Local Loss: 0.0289 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 20.899s | Local Loss: 0.0765 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 14.299s | Local Loss: 0.4778 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 14.023s | Local Loss: 0.0271 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 17.015s | Local Loss: 0.0267 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 14.323s | Local Loss: 0.1015 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 15.485s | Local Loss: 0.4492 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 14.007s | Local Loss: 0.0338 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 15.665s | Local Loss: 0.0791 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 13.729s | Local Loss: 0.2063 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 14.224s | Local Loss: 0.0220 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 14.005s | Local Loss: 0.0184 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 14.310s | Local Loss: 0.0116 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 15.002s | Local Loss: 0.0674 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 14.110s | Local Loss: 0.0174 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 13.877s | Local Loss: 0.0666 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 13.939s | Local Loss: 0.0500 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 14.483s | Local Loss: 0.0371 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 14.900s | Local Loss: 0.2700 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 14.120s | Local Loss: 0.0452 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 14.742s | Local Loss: 0.0151 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 13.770s | Local Loss: 0.0107 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 14.406s | Local Loss: 0.0151 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 13.735s | Local Loss: 0.0264 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 13.918s | Local Loss: 0.1785 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 14.709s | Local Loss: 0.2825 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 13.660s | Local Loss: 0.0208 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 14.656s | Local Loss: 0.0356 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 14.261s | Local Loss: 0.0234 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 14.044s | Local Loss: 0.0385 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 14.572s | Local Loss: 0.0982 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 13.580s | Local Loss: 0.1118 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 14.032s | Local Loss: 0.0222 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 14.791s | Local Loss: 0.0116 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 14.266s | Local Loss: 0.1411 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 14.971s | Local Loss: 0.0889 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 13.555s | Local Loss: 0.3495 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 14.887s | Local Loss: 0.2207 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 14.289s | Local Loss: 0.0157 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 21.834s | Local Loss: 0.1199 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 15.879s | Local Loss: 0.0510 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 14.956s | Local Loss: 0.0856 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 17.146s | Local Loss: 0.3884 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 14.801s | Local Loss: 0.0235 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 15.621s | Local Loss: 0.0211 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 15.001s | Local Loss: 0.0407 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 16.128s | Local Loss: 0.0249 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 16.382s | Local Loss: 0.0175 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 13.531s | Local Loss: 0.0387 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 15.064s | Local Loss: 0.0381 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 13.587s | Local Loss: 0.0396 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 13.494s | Local Loss: 0.0485 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 15.075s | Local Loss: 0.0350 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 13.195s | Local Loss: 0.0409 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 14.677s | Local Loss: 0.0144 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 13.865s | Local Loss: 0.0891 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 13.477s | Local Loss: 0.0182 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 14.901s | Local Loss: 0.0622 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 13.562s | Local Loss: 0.0393 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 13.567s | Local Loss: 0.0456 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 14.361s | Local Loss: 0.0740 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 13.816s | Local Loss: 0.0339 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 14.261s | Local Loss: 0.0128 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 13.455s | Local Loss: 0.0146 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 13.644s | Local Loss: 0.1783 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 14.712s | Local Loss: 0.0208 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 13.702s | Local Loss: 0.0541 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 15.091s | Local Loss: 0.0360 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 14.277s | Local Loss: 0.0235 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 13.612s | Local Loss: 0.0539 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 15.334s | Local Loss: 0.0000 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 13.283s | Local Loss: 0.0806 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 13.920s | Local Loss: 0.0306 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 15.878s | Local Loss: 0.0408 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 13.217s | Local Loss: 0.0243 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 14.102s | Local Loss: 0.0167 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 14.204s | Local Loss: 0.0511 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 13.983s | Local Loss: 0.0150 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 14.011s | Local Loss: 0.0268 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 13.545s | Local Loss: 0.0213 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 14.330s | Local Loss: 0.0157 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 14.676s | Local Loss: 0.2254 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 14.122s | Local Loss: 0.1149 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 13.894s | Local Loss: 0.0265 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 13.604s | Local Loss: 0.0219 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 13.620s | Local Loss: 0.1180 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 14.587s | Local Loss: 0.0129 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 14.200s | Local Loss: 0.0174 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 14.290s | Local Loss: 0.0309 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 24.558s | Local Loss: 0.0139 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 19.267s | Local Loss: 0.0050 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 17.376s | Local Loss: 0.0286 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 19.423s | Local Loss: 0.0036 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 18.994s | Local Loss: 0.0206 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 20.645s | Local Loss: 0.0218 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 17.728s | Local Loss: 0.0163 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 19.087s | Local Loss: 0.3303 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 17.706s | Local Loss: 0.0206 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 18.763s | Local Loss: 0.0578 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 17.708s | Local Loss: 0.0115 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 17.388s | Local Loss: 0.0502 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 17.730s | Local Loss: 0.0088 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 17.353s | Local Loss: 0.1199 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 18.666s | Local Loss: 0.0096 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 13.607s | Local Loss: 0.0083 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 14.162s | Local Loss: 0.3683 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 14.427s | Local Loss: 0.0130 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 14.719s | Local Loss: 0.1802 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 14.377s | Local Loss: 0.0166 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 13.667s | Local Loss: 0.0891 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 13.842s | Local Loss: 0.0227 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 14.522s | Local Loss: 0.0112 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 16.893s | Local Loss: 0.0206 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 15.945s | Local Loss: 0.0256 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 15.653s | Local Loss: 0.0326 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 17.297s | Local Loss: 0.0507 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 15.041s | Local Loss: 0.0220 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 14.517s | Local Loss: 0.0720 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 14.987s | Local Loss: 0.0684 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 15.184s | Local Loss: 0.0646 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 15.255s | Local Loss: 0.0213 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 14.942s | Local Loss: 0.0400 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 13.928s | Local Loss: 0.2422 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 15.398s | Local Loss: 0.0162 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 14.211s | Local Loss: 0.0779 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 14.607s | Local Loss: 0.0241 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 15.127s | Local Loss: 0.0431 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 14.841s | Local Loss: 0.0214 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 16.718s | Local Loss: 0.0102 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 15.964s | Local Loss: 0.0210 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 15.554s | Local Loss: 0.0172 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 14.872s | Local Loss: 0.0090 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 14.773s | Local Loss: 0.0222 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 14.514s | Local Loss: 0.0114 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 14.000s | Local Loss: 0.0697 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 14.521s | Local Loss: 0.0323 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 14.052s | Local Loss: 0.0162 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 14.594s | Local Loss: 0.1456 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 14.010s | Local Loss: 0.1144 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 19.283s | Local Loss: 0.0627 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 17.115s | Local Loss: 0.0941 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 17.701s | Local Loss: 0.0488 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 16.945s | Local Loss: 0.0884 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 17.476s | Local Loss: 0.0165 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 17.036s | Local Loss: 0.0557 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 15.517s | Local Loss: 0.0142 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 16.869s | Local Loss: 0.0155 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 14.825s | Local Loss: 0.0127 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 19.529s | Local Loss: 0.0201 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 23.697s | Local Loss: 0.0271 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 17.029s | Local Loss: 0.0082 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 15.139s | Local Loss: 0.0132 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 15.110s | Local Loss: 0.0822 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 15.345s | Local Loss: 0.0159 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 15.214s | Local Loss: 0.0420 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 15.665s | Local Loss: 0.0051 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 15.585s | Local Loss: 0.0679 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 15.521s | Local Loss: 0.0076 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 13.893s | Local Loss: 0.0130 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 13.413s | Local Loss: 0.0117 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 13.154s | Local Loss: 0.0288 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 13.879s | Local Loss: 0.2713 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 14.568s | Local Loss: 0.0210 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 14.831s | Local Loss: 0.0194 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 14.418s | Local Loss: 0.0077 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 14.034s | Local Loss: 0.2921 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 13.665s | Local Loss: 0.0033 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 13.297s | Local Loss: 0.0143 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 15.059s | Local Loss: 0.0083 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 13.151s | Local Loss: 0.1474 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 13.963s | Local Loss: 0.0284 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 13.524s | Local Loss: 0.0116 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 13.373s | Local Loss: 0.0104 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 14.658s | Local Loss: 0.0394 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 13.443s | Local Loss: 0.0121 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 13.120s | Local Loss: 0.0659 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 14.611s | Local Loss: 0.0295 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 13.466s | Local Loss: 0.0287 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 13.659s | Local Loss: 0.0114 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 13.845s | Local Loss: 0.2949 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 13.533s | Local Loss: 0.0140 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 14.229s | Local Loss: 0.0131 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 13.437s | Local Loss: 0.0000 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 13.547s | Local Loss: 0.3054 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 14.028s | Local Loss: 0.0335 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 13.199s | Local Loss: 0.0074 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 14.437s | Local Loss: 0.2645 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 13.169s | Local Loss: 0.0341 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 13.339s | Local Loss: 0.0064 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 21.653s | Local Loss: 0.0441 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 17.498s | Local Loss: 0.0223 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 16.894s | Local Loss: 0.0243 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 17.350s | Local Loss: 0.0528 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 17.134s | Local Loss: 0.0148 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 17.913s | Local Loss: 0.0355 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 18.555s | Local Loss: 0.0400 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 14.745s | Local Loss: 0.0160 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 13.167s | Local Loss: 0.0395 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 14.376s | Local Loss: 0.0273 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 13.713s | Local Loss: 0.1048 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 13.576s | Local Loss: 0.0106 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 13.454s | Local Loss: 0.0297 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 14.217s | Local Loss: 0.0260 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 13.974s | Local Loss: 0.0160 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 13.429s | Local Loss: 0.0082 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 14.243s | Local Loss: 0.0096 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 13.761s | Local Loss: 0.0238 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 13.204s | Local Loss: 0.0123 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 14.377s | Local Loss: 0.0070 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 14.052s | Local Loss: 0.0182 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 13.473s | Local Loss: 0.2674 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 13.451s | Local Loss: 0.0106 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 13.083s | Local Loss: 0.0351 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 13.462s | Local Loss: 0.0140 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 13.877s | Local Loss: 0.2845 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 13.024s | Local Loss: 0.0088 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 13.331s | Local Loss: 0.0114 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 13.611s | Local Loss: 0.0164 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 13.927s | Local Loss: 0.0121 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 13.746s | Local Loss: 0.0165 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 13.853s | Local Loss: 0.0126 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 14.175s | Local Loss: 0.0892 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 13.657s | Local Loss: 0.0694 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 14.756s | Local Loss: 0.1190 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 13.182s | Local Loss: 0.0168 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 13.755s | Local Loss: 0.0238 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 13.729s | Local Loss: 0.0313 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 13.588s | Local Loss: 0.0377 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 13.922s | Local Loss: 0.0134 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 13.129s | Local Loss: 0.0172 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 14.162s | Local Loss: 0.0244 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 14.019s | Local Loss: 0.0416 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 13.282s | Local Loss: 0.0434 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 14.016s | Local Loss: 0.0133 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 13.428s | Local Loss: 0.1490 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 14.188s | Local Loss: 0.0117 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 13.527s | Local Loss: 0.0070 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 13.701s | Local Loss: 0.0247 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 14.720s | Local Loss: 0.0804 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 21.254s | Local Loss: 0.0119 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 17.178s | Local Loss: 0.0271 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 16.131s | Local Loss: 0.0113 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 17.058s | Local Loss: 0.0119 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 17.187s | Local Loss: 0.0322 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 16.965s | Local Loss: 0.3156 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 18.732s | Local Loss: 0.2794 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 18.632s | Local Loss: 0.0065 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 18.232s | Local Loss: 0.0235 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 17.430s | Local Loss: 0.0099 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 17.977s | Local Loss: 0.0586 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 18.466s | Local Loss: 0.0898 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 14.461s | Local Loss: 0.0068 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 13.718s | Local Loss: 0.0084 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 13.845s | Local Loss: 0.0092 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 14.247s | Local Loss: 0.0371 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 13.486s | Local Loss: 0.0749 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 13.589s | Local Loss: 0.0103 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 15.727s | Local Loss: 0.0116 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 13.265s | Local Loss: 0.0062 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 14.103s | Local Loss: 0.2079 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 14.130s | Local Loss: 0.0334 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 13.433s | Local Loss: 0.2029 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 14.310s | Local Loss: 0.0055 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 13.177s | Local Loss: 0.0094 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 13.990s | Local Loss: 0.0109 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 13.845s | Local Loss: 0.0146 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 14.814s | Local Loss: 0.0178 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 13.300s | Local Loss: 0.0079 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 14.419s | Local Loss: 0.0120 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 14.957s | Local Loss: 0.0162 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 14.178s | Local Loss: 0.0106 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 14.345s | Local Loss: 0.0125 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 14.510s | Local Loss: 0.0162 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 14.267s | Local Loss: 0.1398 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 13.745s | Local Loss: 0.0111 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 14.849s | Local Loss: 0.0336 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 14.599s | Local Loss: 0.0088 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 13.663s | Local Loss: 0.1695 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 14.250s | Local Loss: 0.0208 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 14.282s | Local Loss: 0.0066 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 13.447s | Local Loss: 0.0083 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 13.286s | Local Loss: 0.0885 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 15.709s | Local Loss: 0.0729 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 12.961s | Local Loss: 0.0116 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 13.574s | Local Loss: 0.0227 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 13.815s | Local Loss: 0.0077 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 13.670s | Local Loss: 0.0130 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 14.252s | Local Loss: 0.0109 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 13.401s | Local Loss: 0.0091 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 22:41:07] Logger closing.
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 14.601s | Local Loss: 0.1352 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 13.976s | Local Loss: 0.5310 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.265s | Local Loss: 0.2672 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.061s | Local Loss: 0.2285 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 12.948s | Local Loss: 0.1747 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 11.955s | Local Loss: 0.2394 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.197s | Local Loss: 0.2723 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 12.388s | Local Loss: 0.2773 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.799s | Local Loss: 0.3338 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 15.040s | Local Loss: 0.1227 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 14.077s | Local Loss: 0.2415 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.163s | Local Loss: 0.2657 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 13.584s | Local Loss: 0.4104 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.070s | Local Loss: 0.2866 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 12.904s | Local Loss: 0.2150 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.304s | Local Loss: 0.1208 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 11.857s | Local Loss: 0.4731 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 11.900s | Local Loss: 0.3123 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 11.765s | Local Loss: 0.1280 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 11.784s | Local Loss: 0.0369 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 12.923s | Local Loss: 0.5713 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 11.613s | Local Loss: 0.3022 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 12.953s | Local Loss: 0.1257 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 11.909s | Local Loss: 0.3741 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.106s | Local Loss: 0.2674 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.427s | Local Loss: 0.1027 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 11.823s | Local Loss: 0.1804 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 11.670s | Local Loss: 0.0263 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.153s | Local Loss: 0.0620 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.247s | Local Loss: 0.0151 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.364s | Local Loss: 0.4905 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 13.066s | Local Loss: 0.2030 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 11.369s | Local Loss: 0.0585 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 11.554s | Local Loss: 0.3373 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.195s | Local Loss: 0.4124 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.161s | Local Loss: 0.0344 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 11.619s | Local Loss: 0.2007 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.112s | Local Loss: 0.1522 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 11.816s | Local Loss: 0.2097 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 11.627s | Local Loss: 0.3352 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 11.969s | Local Loss: 0.1246 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.447s | Local Loss: 0.0542 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 11.963s | Local Loss: 0.1257 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 12.800s | Local Loss: 0.2827 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 11.765s | Local Loss: 0.0345 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 11.813s | Local Loss: 0.1189 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 11.816s | Local Loss: 0.1573 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 11.738s | Local Loss: 0.2256 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 11.628s | Local Loss: 0.0345 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 11.837s | Local Loss: 0.2343 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 20.839s | Local Loss: 0.2394 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 15.264s | Local Loss: 0.0681 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 15.576s | Local Loss: 0.0400 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 13.697s | Local Loss: 0.0299 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 13.267s | Local Loss: 0.1257 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 13.756s | Local Loss: 0.0511 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 14.733s | Local Loss: 0.1527 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 13.051s | Local Loss: 0.1182 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 13.123s | Local Loss: 0.1498 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 13.468s | Local Loss: 0.0754 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 13.268s | Local Loss: 0.3338 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 12.881s | Local Loss: 0.0280 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 13.379s | Local Loss: 0.0679 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 12.948s | Local Loss: 0.2825 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 12.978s | Local Loss: 0.3510 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 12.994s | Local Loss: 0.1046 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 12.826s | Local Loss: 0.0532 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 13.191s | Local Loss: 0.0889 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 12.623s | Local Loss: 0.3311 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 12.840s | Local Loss: 0.0726 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 12.716s | Local Loss: 0.0191 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 12.896s | Local Loss: 0.3472 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 13.254s | Local Loss: 0.0644 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 13.073s | Local Loss: 0.0469 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 13.051s | Local Loss: 0.1019 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 13.141s | Local Loss: 0.3495 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 12.828s | Local Loss: 0.1115 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 12.910s | Local Loss: 0.3087 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 13.361s | Local Loss: 0.4391 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 12.809s | Local Loss: 0.2145 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 12.767s | Local Loss: 0.0216 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 12.773s | Local Loss: 0.1379 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 12.715s | Local Loss: 0.2626 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 13.115s | Local Loss: 0.0496 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 12.727s | Local Loss: 0.1422 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 12.740s | Local Loss: 0.0555 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 13.347s | Local Loss: 0.2970 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 12.783s | Local Loss: 0.1683 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 12.864s | Local Loss: 0.0431 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 12.678s | Local Loss: 0.0475 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 12.549s | Local Loss: 0.1540 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 13.385s | Local Loss: 0.4583 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 12.642s | Local Loss: 0.0485 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 12.911s | Local Loss: 0.0541 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 13.001s | Local Loss: 0.0375 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 12.753s | Local Loss: 0.0519 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 13.320s | Local Loss: 0.2699 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 12.622s | Local Loss: 0.0303 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 12.792s | Local Loss: 0.0160 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 13.035s | Local Loss: 0.1623 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 19.901s | Local Loss: 0.3314 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 16.894s | Local Loss: 0.4630 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 17.992s | Local Loss: 0.0000 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 16.066s | Local Loss: 0.2999 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 16.951s | Local Loss: 0.5363 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 17.254s | Local Loss: 0.0303 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 17.129s | Local Loss: 0.1020 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 18.170s | Local Loss: 0.4122 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 13.429s | Local Loss: 0.2951 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 14.111s | Local Loss: 0.3120 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 13.394s | Local Loss: 0.1426 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 13.637s | Local Loss: 0.1516 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 13.328s | Local Loss: 0.0903 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 13.169s | Local Loss: 0.0573 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 13.434s | Local Loss: 0.3301 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 13.241s | Local Loss: 0.1405 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 13.497s | Local Loss: 0.1878 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 13.388s | Local Loss: 0.0361 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 14.249s | Local Loss: 0.2075 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 13.462s | Local Loss: 0.0281 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 13.654s | Local Loss: 0.0561 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 13.462s | Local Loss: 0.0771 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 13.339s | Local Loss: 0.2001 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 13.270s | Local Loss: 0.0213 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 13.321s | Local Loss: 0.0129 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 14.058s | Local Loss: 0.0617 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 13.469s | Local Loss: 0.0153 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 13.383s | Local Loss: 0.0133 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 13.783s | Local Loss: 0.2150 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 13.563s | Local Loss: 0.3351 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 13.344s | Local Loss: 0.0614 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 13.226s | Local Loss: 0.2417 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 13.509s | Local Loss: 0.2659 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 13.731s | Local Loss: 0.0373 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 13.465s | Local Loss: 0.0496 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 12.952s | Local Loss: 0.1398 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 13.557s | Local Loss: 0.0225 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 13.413s | Local Loss: 0.2140 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 13.449s | Local Loss: 0.3399 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 13.493s | Local Loss: 0.2793 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 13.797s | Local Loss: 0.0446 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 13.908s | Local Loss: 0.0763 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 13.731s | Local Loss: 0.1010 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 13.201s | Local Loss: 0.1382 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 13.877s | Local Loss: 0.0224 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 13.381s | Local Loss: 0.0758 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 13.379s | Local Loss: 0.0172 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 13.208s | Local Loss: 0.4348 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 13.562s | Local Loss: 0.1439 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 13.743s | Local Loss: 0.1978 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 19.581s | Local Loss: 0.0926 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 16.777s | Local Loss: 0.3100 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 17.809s | Local Loss: 0.0541 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 16.659s | Local Loss: 0.0088 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 17.686s | Local Loss: 0.0952 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 16.335s | Local Loss: 0.0205 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 17.219s | Local Loss: 0.0926 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 17.345s | Local Loss: 0.1259 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 17.930s | Local Loss: 0.0602 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 16.670s | Local Loss: 0.0654 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 19.125s | Local Loss: 0.0710 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 13.210s | Local Loss: 0.0718 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 14.083s | Local Loss: 0.0457 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 13.489s | Local Loss: 0.0232 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 13.630s | Local Loss: 0.0478 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 13.587s | Local Loss: 0.0523 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 12.889s | Local Loss: 0.0477 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 13.645s | Local Loss: 0.0555 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 13.348s | Local Loss: 0.1714 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 13.204s | Local Loss: 0.4355 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 13.470s | Local Loss: 0.0378 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 13.071s | Local Loss: 0.0325 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 13.341s | Local Loss: 0.0771 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 13.153s | Local Loss: 0.0396 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 13.276s | Local Loss: 0.0477 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 13.650s | Local Loss: 0.4938 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 13.427s | Local Loss: 0.1092 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 12.861s | Local Loss: 0.0881 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 13.763s | Local Loss: 0.0441 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 13.702s | Local Loss: 0.4379 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 13.446s | Local Loss: 0.0512 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 13.489s | Local Loss: 0.0213 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 13.732s | Local Loss: 0.0395 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 13.679s | Local Loss: 0.0880 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 13.737s | Local Loss: 0.1473 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 13.379s | Local Loss: 0.1221 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 13.616s | Local Loss: 0.2844 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 13.400s | Local Loss: 0.2896 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 13.345s | Local Loss: 0.1095 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 13.621s | Local Loss: 0.0169 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 13.461s | Local Loss: 0.0423 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 13.208s | Local Loss: 0.0249 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 13.663s | Local Loss: 0.0589 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 13.472s | Local Loss: 0.4108 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 13.632s | Local Loss: 0.0177 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 13.044s | Local Loss: 0.0182 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 13.333s | Local Loss: 0.0394 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 13.755s | Local Loss: 0.0295 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 13.828s | Local Loss: 0.2287 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 13.141s | Local Loss: 0.0334 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 17.955s | Local Loss: 0.0409 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 15.298s | Local Loss: 0.4127 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 15.043s | Local Loss: 0.0080 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 15.147s | Local Loss: 0.0247 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 15.300s | Local Loss: 0.0801 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 15.002s | Local Loss: 0.3881 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 15.394s | Local Loss: 0.0337 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 15.248s | Local Loss: 0.0267 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 14.786s | Local Loss: 0.0276 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 14.896s | Local Loss: 0.0342 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 15.344s | Local Loss: 0.0319 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 15.263s | Local Loss: 0.0289 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 14.778s | Local Loss: 0.0770 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 15.274s | Local Loss: 0.4770 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 15.170s | Local Loss: 0.0321 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 15.156s | Local Loss: 0.0262 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 14.527s | Local Loss: 0.1028 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 16.483s | Local Loss: 0.4512 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 13.442s | Local Loss: 0.0285 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 13.729s | Local Loss: 0.0797 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 13.519s | Local Loss: 0.2083 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 13.485s | Local Loss: 0.0193 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 13.235s | Local Loss: 0.0182 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 13.597s | Local Loss: 0.0111 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 13.163s | Local Loss: 0.0686 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 13.646s | Local Loss: 0.0145 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 13.565s | Local Loss: 0.0670 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 13.356s | Local Loss: 0.0505 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 13.406s | Local Loss: 0.0382 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 13.410s | Local Loss: 0.2730 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 13.136s | Local Loss: 0.0464 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 13.572s | Local Loss: 0.0158 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 13.614s | Local Loss: 0.0097 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 13.644s | Local Loss: 0.0154 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 13.390s | Local Loss: 0.0409 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 13.630s | Local Loss: 0.1822 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 13.603s | Local Loss: 0.2826 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 13.545s | Local Loss: 0.0239 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 13.730s | Local Loss: 0.0376 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 13.273s | Local Loss: 0.0208 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 13.362s | Local Loss: 0.0416 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 13.920s | Local Loss: 0.1041 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 13.584s | Local Loss: 0.1183 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 13.270s | Local Loss: 0.0263 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 13.871s | Local Loss: 0.0128 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 13.576s | Local Loss: 0.1450 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 13.349s | Local Loss: 0.0913 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 13.739s | Local Loss: 0.3507 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 13.422s | Local Loss: 0.2214 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 13.401s | Local Loss: 0.0179 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 19.853s | Local Loss: 0.1247 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 17.051s | Local Loss: 0.0525 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 17.623s | Local Loss: 0.0868 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 17.197s | Local Loss: 0.3887 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 17.400s | Local Loss: 0.0248 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 18.323s | Local Loss: 0.0117 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 14.368s | Local Loss: 0.0416 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 13.633s | Local Loss: 0.0248 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 13.503s | Local Loss: 0.0174 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 13.889s | Local Loss: 0.0394 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 13.299s | Local Loss: 0.0376 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 13.640s | Local Loss: 0.0390 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 13.531s | Local Loss: 0.0491 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 13.901s | Local Loss: 0.0290 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 13.564s | Local Loss: 0.0415 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 13.449s | Local Loss: 0.0166 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 13.075s | Local Loss: 0.0893 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 14.522s | Local Loss: 0.0158 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 13.007s | Local Loss: 0.0628 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 13.146s | Local Loss: 0.0427 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 13.417s | Local Loss: 0.0460 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 13.325s | Local Loss: 0.0753 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 16.860s | Local Loss: 0.0348 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 20.586s | Local Loss: 0.0139 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 20.107s | Local Loss: 0.0127 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 20.550s | Local Loss: 0.1792 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 20.439s | Local Loss: 0.0231 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 21.119s | Local Loss: 0.0542 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 20.661s | Local Loss: 0.0360 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 20.236s | Local Loss: 0.0266 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 20.952s | Local Loss: 0.0541 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 20.283s | Local Loss: 0.0000 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 21.429s | Local Loss: 0.0800 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 20.140s | Local Loss: 0.0302 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 20.943s | Local Loss: 0.0420 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 20.577s | Local Loss: 0.0213 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 21.603s | Local Loss: 0.0210 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 20.109s | Local Loss: 0.0563 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 22.185s | Local Loss: 0.0130 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 20.831s | Local Loss: 0.0269 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 25.000s | Local Loss: 0.0213 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 20.038s | Local Loss: 0.0187 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 21.304s | Local Loss: 0.2275 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 20.881s | Local Loss: 0.1203 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 21.384s | Local Loss: 0.0275 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 16.039s | Local Loss: 0.0222 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 13.070s | Local Loss: 0.1177 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 13.376s | Local Loss: 0.0208 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 13.440s | Local Loss: 0.0167 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 13.411s | Local Loss: 0.0313 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 17.978s | Local Loss: 0.0129 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 15.478s | Local Loss: 0.0049 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 15.479s | Local Loss: 0.0298 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 15.104s | Local Loss: 0.0039 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 14.793s | Local Loss: 0.0154 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 15.399s | Local Loss: 0.0215 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 15.061s | Local Loss: 0.0173 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 15.277s | Local Loss: 0.3321 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 14.853s | Local Loss: 0.0198 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 15.044s | Local Loss: 0.0599 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 15.794s | Local Loss: 0.0118 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 13.567s | Local Loss: 0.0508 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 13.345s | Local Loss: 0.0086 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 13.358s | Local Loss: 0.1164 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 13.150s | Local Loss: 0.0087 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 13.251s | Local Loss: 0.0080 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 13.948s | Local Loss: 0.3698 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 13.269s | Local Loss: 0.0107 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 13.298s | Local Loss: 0.1822 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 13.684s | Local Loss: 0.0145 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 13.569s | Local Loss: 0.0898 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 14.104s | Local Loss: 0.0225 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 14.592s | Local Loss: 0.0117 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 14.036s | Local Loss: 0.0205 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 14.270s | Local Loss: 0.0262 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 14.354s | Local Loss: 0.0310 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 14.249s | Local Loss: 0.0513 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 13.693s | Local Loss: 0.0229 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 13.855s | Local Loss: 0.0711 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 13.447s | Local Loss: 0.0657 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 13.891s | Local Loss: 0.0613 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 13.528s | Local Loss: 0.0145 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 13.816s | Local Loss: 0.0386 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 13.827s | Local Loss: 0.2389 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 13.549s | Local Loss: 0.0159 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 13.632s | Local Loss: 0.0757 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 14.115s | Local Loss: 0.0252 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 13.349s | Local Loss: 0.0432 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 13.393s | Local Loss: 0.0225 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 13.995s | Local Loss: 0.0101 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 13.272s | Local Loss: 0.0175 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 13.374s | Local Loss: 0.0154 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 13.377s | Local Loss: 0.0088 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 13.744s | Local Loss: 0.0223 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 13.954s | Local Loss: 0.0136 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 14.179s | Local Loss: 0.0668 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 13.750s | Local Loss: 0.0126 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 14.017s | Local Loss: 0.0150 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 13.437s | Local Loss: 0.1433 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 13.714s | Local Loss: 0.1138 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 19.723s | Local Loss: 0.0602 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 17.608s | Local Loss: 0.0944 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 16.664s | Local Loss: 0.0509 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 18.011s | Local Loss: 0.0882 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 16.990s | Local Loss: 0.0163 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 19.268s | Local Loss: 0.0560 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 17.369s | Local Loss: 0.0173 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 18.211s | Local Loss: 0.0137 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 17.312s | Local Loss: 0.0141 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 17.610s | Local Loss: 0.0180 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 18.094s | Local Loss: 0.0283 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 18.288s | Local Loss: 0.0088 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 16.977s | Local Loss: 0.0165 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 25.209s | Local Loss: 0.0834 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 20.340s | Local Loss: 0.0178 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 18.821s | Local Loss: 0.0441 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 14.334s | Local Loss: 0.0069 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 13.601s | Local Loss: 0.0706 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 12.930s | Local Loss: 0.0079 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 13.152s | Local Loss: 0.0144 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 13.343s | Local Loss: 0.0155 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 12.501s | Local Loss: 0.0284 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 13.118s | Local Loss: 0.2728 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 12.919s | Local Loss: 0.0219 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 12.886s | Local Loss: 0.0198 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 13.318s | Local Loss: 0.0098 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 12.891s | Local Loss: 0.2926 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 13.213s | Local Loss: 0.0047 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 13.449s | Local Loss: 0.0158 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 12.592s | Local Loss: 0.0084 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 13.088s | Local Loss: 0.1566 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 13.031s | Local Loss: 0.0295 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 13.173s | Local Loss: 0.0154 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 12.779s | Local Loss: 0.0110 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 12.964s | Local Loss: 0.0405 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 13.159s | Local Loss: 0.0142 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 12.635s | Local Loss: 0.0659 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 13.059s | Local Loss: 0.0301 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 12.761s | Local Loss: 0.0295 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 13.073s | Local Loss: 0.0140 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 12.971s | Local Loss: 0.2998 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 13.752s | Local Loss: 0.0144 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 16.267s | Local Loss: 0.0133 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 14.054s | Local Loss: 0.0000 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 14.083s | Local Loss: 0.3044 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 13.271s | Local Loss: 0.0350 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 14.055s | Local Loss: 0.0075 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_7.log b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_7.log
new file mode 100644
index 0000000000000000000000000000000000000000..003a5915b1041631e79ac7eb3b2606e66346d1f3
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/log/rank_7.log
@@ -0,0 +1,900 @@
+[2026-01-11 18:53:34] Logger initialized. Current global_step: 0
+[2026-01-11 18:54:31] Epoch: 0 | Iter: 000000 | Step Time: 17.888s | Local Loss: 0.1366 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-11 18:54:50] Epoch: 1 | Iter: 000001 | Step Time: 14.650s | Local Loss: 0.2652 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-11 18:55:09] Epoch: 2 | Iter: 000002 | Step Time: 14.107s | Local Loss: 0.0397 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-11 18:55:26] Epoch: 3 | Iter: 000003 | Step Time: 13.451s | Local Loss: 0.0851 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-11 18:55:43] Epoch: 4 | Iter: 000004 | Step Time: 12.522s | Local Loss: 0.0737 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-11 18:56:03] Epoch: 5 | Iter: 000005 | Step Time: 15.001s | Local Loss: 0.3566 | Avg Loss: 0.2454 | LR: 0.000010
+[2026-01-11 18:56:31] Epoch: 6 | Iter: 000006 | Step Time: 23.501s | Local Loss: 0.1337 | Avg Loss: 0.1623 | LR: 0.000010
+[2026-01-11 18:56:49] Epoch: 7 | Iter: 000007 | Step Time: 14.387s | Local Loss: 0.1025 | Avg Loss: 0.2099 | LR: 0.000010
+[2026-01-11 18:57:08] Epoch: 8 | Iter: 000008 | Step Time: 14.479s | Local Loss: 0.3538 | Avg Loss: 0.1679 | LR: 0.000010
+[2026-01-11 18:57:26] Epoch: 9 | Iter: 000009 | Step Time: 13.598s | Local Loss: 0.3678 | Avg Loss: 0.2817 | LR: 0.000010
+[2026-01-11 18:57:45] Epoch: 10 | Iter: 000010 | Step Time: 14.249s | Local Loss: 0.0934 | Avg Loss: 0.1153 | LR: 0.000010
+[2026-01-11 18:58:04] Epoch: 11 | Iter: 000011 | Step Time: 14.591s | Local Loss: 0.2475 | Avg Loss: 0.1755 | LR: 0.000010
+[2026-01-11 18:58:24] Epoch: 12 | Iter: 000012 | Step Time: 15.351s | Local Loss: 0.1538 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-11 18:58:43] Epoch: 13 | Iter: 000013 | Step Time: 15.334s | Local Loss: 0.0269 | Avg Loss: 0.1922 | LR: 0.000010
+[2026-01-11 18:59:01] Epoch: 14 | Iter: 000014 | Step Time: 13.659s | Local Loss: 0.4773 | Avg Loss: 0.1921 | LR: 0.000010
+[2026-01-11 18:59:19] Epoch: 15 | Iter: 000015 | Step Time: 13.050s | Local Loss: 0.1225 | Avg Loss: 0.2415 | LR: 0.000010
+[2026-01-11 18:59:37] Epoch: 16 | Iter: 000016 | Step Time: 13.981s | Local Loss: 0.0322 | Avg Loss: 0.0934 | LR: 0.000010
+[2026-01-11 18:59:57] Epoch: 17 | Iter: 000017 | Step Time: 14.732s | Local Loss: 0.3633 | Avg Loss: 0.1942 | LR: 0.000010
+[2026-01-11 19:00:15] Epoch: 18 | Iter: 000018 | Step Time: 13.828s | Local Loss: 0.0208 | Avg Loss: 0.2322 | LR: 0.000010
+[2026-01-11 19:00:34] Epoch: 19 | Iter: 000019 | Step Time: 14.165s | Local Loss: 0.1693 | Avg Loss: 0.1369 | LR: 0.000010
+[2026-01-11 19:00:51] Epoch: 20 | Iter: 000020 | Step Time: 12.883s | Local Loss: 0.1778 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-11 19:01:09] Epoch: 21 | Iter: 000021 | Step Time: 13.922s | Local Loss: 0.1750 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-11 19:01:27] Epoch: 22 | Iter: 000022 | Step Time: 13.841s | Local Loss: 0.2433 | Avg Loss: 0.1340 | LR: 0.000010
+[2026-01-11 19:01:44] Epoch: 23 | Iter: 000023 | Step Time: 12.368s | Local Loss: 0.0774 | Avg Loss: 0.1461 | LR: 0.000010
+[2026-01-11 19:02:02] Epoch: 24 | Iter: 000024 | Step Time: 12.981s | Local Loss: 0.1233 | Avg Loss: 0.2495 | LR: 0.000010
+[2026-01-11 19:02:19] Epoch: 25 | Iter: 000025 | Step Time: 12.678s | Local Loss: 0.3001 | Avg Loss: 0.1179 | LR: 0.000010
+[2026-01-11 19:02:36] Epoch: 26 | Iter: 000026 | Step Time: 12.296s | Local Loss: 0.2877 | Avg Loss: 0.2027 | LR: 0.000010
+[2026-01-11 19:02:54] Epoch: 27 | Iter: 000027 | Step Time: 13.925s | Local Loss: 0.4702 | Avg Loss: 0.1006 | LR: 0.000010
+[2026-01-11 19:03:11] Epoch: 28 | Iter: 000028 | Step Time: 12.610s | Local Loss: 0.2087 | Avg Loss: 0.1741 | LR: 0.000010
+[2026-01-11 19:03:29] Epoch: 29 | Iter: 000029 | Step Time: 13.731s | Local Loss: 0.0826 | Avg Loss: 0.3759 | LR: 0.000010
+[2026-01-11 19:03:47] Epoch: 30 | Iter: 000030 | Step Time: 13.461s | Local Loss: 0.5363 | Avg Loss: 0.1036 | LR: 0.000010
+[2026-01-11 19:04:04] Epoch: 31 | Iter: 000031 | Step Time: 12.582s | Local Loss: 0.1332 | Avg Loss: 0.0601 | LR: 0.000010
+[2026-01-11 19:04:22] Epoch: 32 | Iter: 000032 | Step Time: 13.080s | Local Loss: 0.0733 | Avg Loss: 0.1948 | LR: 0.000010
+[2026-01-11 19:04:40] Epoch: 33 | Iter: 000033 | Step Time: 14.290s | Local Loss: 0.3472 | Avg Loss: 0.1142 | LR: 0.000010
+[2026-01-11 19:04:58] Epoch: 34 | Iter: 000034 | Step Time: 12.967s | Local Loss: 0.0500 | Avg Loss: 0.2037 | LR: 0.000010
+[2026-01-11 19:05:15] Epoch: 35 | Iter: 000035 | Step Time: 13.055s | Local Loss: 0.0422 | Avg Loss: 0.1272 | LR: 0.000010
+[2026-01-11 19:05:34] Epoch: 36 | Iter: 000036 | Step Time: 14.048s | Local Loss: 0.4733 | Avg Loss: 0.1858 | LR: 0.000010
+[2026-01-11 19:05:51] Epoch: 37 | Iter: 000037 | Step Time: 12.619s | Local Loss: 0.0872 | Avg Loss: 0.2433 | LR: 0.000010
+[2026-01-11 19:06:08] Epoch: 38 | Iter: 000038 | Step Time: 12.524s | Local Loss: 0.5418 | Avg Loss: 0.2056 | LR: 0.000010
+[2026-01-11 19:06:25] Epoch: 39 | Iter: 000039 | Step Time: 13.410s | Local Loss: 0.1494 | Avg Loss: 0.1467 | LR: 0.000010
+[2026-01-11 19:06:44] Epoch: 40 | Iter: 000040 | Step Time: 13.674s | Local Loss: 0.0511 | Avg Loss: 0.1287 | LR: 0.000010
+[2026-01-11 19:07:02] Epoch: 41 | Iter: 000041 | Step Time: 13.550s | Local Loss: 0.5403 | Avg Loss: 0.2439 | LR: 0.000010
+[2026-01-11 19:07:20] Epoch: 42 | Iter: 000042 | Step Time: 13.727s | Local Loss: 0.1400 | Avg Loss: 0.2397 | LR: 0.000010
+[2026-01-11 19:07:38] Epoch: 43 | Iter: 000043 | Step Time: 13.356s | Local Loss: 0.1087 | Avg Loss: 0.0808 | LR: 0.000010
+[2026-01-11 19:07:55] Epoch: 44 | Iter: 000044 | Step Time: 12.908s | Local Loss: 0.0511 | Avg Loss: 0.2266 | LR: 0.000010
+[2026-01-11 19:08:12] Epoch: 45 | Iter: 000045 | Step Time: 12.928s | Local Loss: 0.2673 | Avg Loss: 0.1004 | LR: 0.000010
+[2026-01-11 19:08:30] Epoch: 46 | Iter: 000046 | Step Time: 13.008s | Local Loss: 0.3769 | Avg Loss: 0.0749 | LR: 0.000010
+[2026-01-11 19:08:47] Epoch: 47 | Iter: 000047 | Step Time: 12.932s | Local Loss: 0.3162 | Avg Loss: 0.2486 | LR: 0.000010
+[2026-01-11 19:09:05] Epoch: 48 | Iter: 000048 | Step Time: 13.481s | Local Loss: 0.3493 | Avg Loss: 0.1350 | LR: 0.000010
+[2026-01-11 19:09:23] Epoch: 49 | Iter: 000049 | Step Time: 13.105s | Local Loss: 0.1338 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-11 19:12:21] Epoch: 50 | Iter: 000050 | Step Time: 21.181s | Local Loss: 0.0492 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 19:12:45] Epoch: 51 | Iter: 000051 | Step Time: 17.741s | Local Loss: 0.5209 | Avg Loss: 0.1032 | LR: 0.000010
+[2026-01-11 19:13:08] Epoch: 52 | Iter: 000052 | Step Time: 17.117s | Local Loss: 0.1585 | Avg Loss: 0.1924 | LR: 0.000010
+[2026-01-11 19:13:32] Epoch: 53 | Iter: 000053 | Step Time: 17.620s | Local Loss: 0.4276 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-11 19:13:51] Epoch: 54 | Iter: 000054 | Step Time: 14.286s | Local Loss: 0.1678 | Avg Loss: 0.0666 | LR: 0.000010
+[2026-01-11 19:14:10] Epoch: 55 | Iter: 000055 | Step Time: 13.812s | Local Loss: 0.0325 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 19:14:29] Epoch: 56 | Iter: 000056 | Step Time: 13.765s | Local Loss: 0.4023 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-11 19:14:48] Epoch: 57 | Iter: 000057 | Step Time: 14.316s | Local Loss: 0.0334 | Avg Loss: 0.0818 | LR: 0.000010
+[2026-01-11 19:15:07] Epoch: 58 | Iter: 000058 | Step Time: 13.136s | Local Loss: 0.0220 | Avg Loss: 0.2193 | LR: 0.000010
+[2026-01-11 19:15:25] Epoch: 59 | Iter: 000059 | Step Time: 13.451s | Local Loss: 0.0980 | Avg Loss: 0.2168 | LR: 0.000010
+[2026-01-11 19:15:45] Epoch: 60 | Iter: 000060 | Step Time: 14.404s | Local Loss: 0.1739 | Avg Loss: 0.2877 | LR: 0.000010
+[2026-01-11 19:16:05] Epoch: 61 | Iter: 000061 | Step Time: 15.214s | Local Loss: 0.0504 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-11 19:16:25] Epoch: 62 | Iter: 000062 | Step Time: 14.797s | Local Loss: 0.5611 | Avg Loss: 0.1258 | LR: 0.000010
+[2026-01-11 19:16:44] Epoch: 63 | Iter: 000063 | Step Time: 14.056s | Local Loss: 0.0663 | Avg Loss: 0.3012 | LR: 0.000010
+[2026-01-11 19:17:05] Epoch: 64 | Iter: 000064 | Step Time: 15.269s | Local Loss: 0.2442 | Avg Loss: 0.2040 | LR: 0.000010
+[2026-01-11 19:17:25] Epoch: 65 | Iter: 000065 | Step Time: 14.773s | Local Loss: 0.0752 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:17:46] Epoch: 66 | Iter: 000066 | Step Time: 15.714s | Local Loss: 0.1795 | Avg Loss: 0.2910 | LR: 0.000010
+[2026-01-11 19:18:05] Epoch: 67 | Iter: 000067 | Step Time: 13.868s | Local Loss: 0.0214 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 19:18:24] Epoch: 68 | Iter: 000068 | Step Time: 14.459s | Local Loss: 0.2154 | Avg Loss: 0.1629 | LR: 0.000010
+[2026-01-11 19:18:44] Epoch: 69 | Iter: 000069 | Step Time: 14.308s | Local Loss: 0.2955 | Avg Loss: 0.3629 | LR: 0.000010
+[2026-01-11 19:19:03] Epoch: 70 | Iter: 000070 | Step Time: 13.963s | Local Loss: 0.0395 | Avg Loss: 0.0984 | LR: 0.000010
+[2026-01-11 19:19:22] Epoch: 71 | Iter: 000071 | Step Time: 13.780s | Local Loss: 0.2256 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 19:19:41] Epoch: 72 | Iter: 000072 | Step Time: 13.727s | Local Loss: 0.2371 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 19:20:00] Epoch: 73 | Iter: 000073 | Step Time: 14.398s | Local Loss: 0.0393 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:20:19] Epoch: 74 | Iter: 000074 | Step Time: 13.793s | Local Loss: 0.0430 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:20:39] Epoch: 75 | Iter: 000075 | Step Time: 14.928s | Local Loss: 0.0982 | Avg Loss: 0.1879 | LR: 0.000010
+[2026-01-11 19:20:59] Epoch: 76 | Iter: 000076 | Step Time: 13.869s | Local Loss: 0.0501 | Avg Loss: 0.0642 | LR: 0.000010
+[2026-01-11 19:21:18] Epoch: 77 | Iter: 000077 | Step Time: 14.092s | Local Loss: 0.2114 | Avg Loss: 0.0609 | LR: 0.000010
+[2026-01-11 19:21:37] Epoch: 78 | Iter: 000078 | Step Time: 14.074s | Local Loss: 0.4583 | Avg Loss: 0.1754 | LR: 0.000010
+[2026-01-11 19:21:56] Epoch: 79 | Iter: 000079 | Step Time: 13.507s | Local Loss: 0.3921 | Avg Loss: 0.1286 | LR: 0.000010
+[2026-01-11 19:22:15] Epoch: 80 | Iter: 000080 | Step Time: 13.676s | Local Loss: 0.2869 | Avg Loss: 0.0978 | LR: 0.000010
+[2026-01-11 19:22:34] Epoch: 81 | Iter: 000081 | Step Time: 13.929s | Local Loss: 0.0941 | Avg Loss: 0.1316 | LR: 0.000010
+[2026-01-11 19:22:52] Epoch: 82 | Iter: 000082 | Step Time: 13.225s | Local Loss: 0.0316 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 19:23:12] Epoch: 83 | Iter: 000083 | Step Time: 14.321s | Local Loss: 0.0328 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-11 19:23:31] Epoch: 84 | Iter: 000084 | Step Time: 13.753s | Local Loss: 0.2745 | Avg Loss: 0.1682 | LR: 0.000010
+[2026-01-11 19:23:49] Epoch: 85 | Iter: 000085 | Step Time: 13.220s | Local Loss: 0.0777 | Avg Loss: 0.0684 | LR: 0.000010
+[2026-01-11 19:24:08] Epoch: 86 | Iter: 000086 | Step Time: 14.132s | Local Loss: 0.0712 | Avg Loss: 0.2205 | LR: 0.000010
+[2026-01-11 19:24:27] Epoch: 87 | Iter: 000087 | Step Time: 13.612s | Local Loss: 0.0602 | Avg Loss: 0.1053 | LR: 0.000010
+[2026-01-11 19:24:46] Epoch: 88 | Iter: 000088 | Step Time: 13.882s | Local Loss: 0.3940 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 19:25:05] Epoch: 89 | Iter: 000089 | Step Time: 13.512s | Local Loss: 0.0373 | Avg Loss: 0.0697 | LR: 0.000010
+[2026-01-11 19:25:24] Epoch: 90 | Iter: 000090 | Step Time: 13.831s | Local Loss: 0.0638 | Avg Loss: 0.1783 | LR: 0.000010
+[2026-01-11 19:25:43] Epoch: 91 | Iter: 000091 | Step Time: 13.695s | Local Loss: 0.1631 | Avg Loss: 0.1056 | LR: 0.000010
+[2026-01-11 19:26:02] Epoch: 92 | Iter: 000092 | Step Time: 13.662s | Local Loss: 0.0423 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-11 19:26:22] Epoch: 93 | Iter: 000093 | Step Time: 14.176s | Local Loss: 0.0190 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 19:26:40] Epoch: 94 | Iter: 000094 | Step Time: 13.555s | Local Loss: 0.0724 | Avg Loss: 0.1173 | LR: 0.000010
+[2026-01-11 19:26:59] Epoch: 95 | Iter: 000095 | Step Time: 13.608s | Local Loss: 0.3893 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:27:18] Epoch: 96 | Iter: 000096 | Step Time: 13.471s | Local Loss: 0.0517 | Avg Loss: 0.1635 | LR: 0.000010
+[2026-01-11 19:27:37] Epoch: 97 | Iter: 000097 | Step Time: 14.378s | Local Loss: 0.4600 | Avg Loss: 0.1202 | LR: 0.000010
+[2026-01-11 19:27:56] Epoch: 98 | Iter: 000098 | Step Time: 13.446s | Local Loss: 0.0058 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-11 19:28:15] Epoch: 99 | Iter: 000099 | Step Time: 13.691s | Local Loss: 0.0861 | Avg Loss: 0.0843 | LR: 0.000010
+[2026-01-11 19:31:15] Epoch: 100 | Iter: 000100 | Step Time: 20.758s | Local Loss: 0.0577 | Avg Loss: 0.1720 | LR: 0.000010
+[2026-01-11 19:31:38] Epoch: 101 | Iter: 000101 | Step Time: 17.338s | Local Loss: 0.3671 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:31:59] Epoch: 102 | Iter: 000102 | Step Time: 15.019s | Local Loss: 0.0457 | Avg Loss: 0.0388 | LR: 0.000010
+[2026-01-11 19:32:19] Epoch: 103 | Iter: 000103 | Step Time: 14.972s | Local Loss: 0.3407 | Avg Loss: 0.0904 | LR: 0.000010
+[2026-01-11 19:32:40] Epoch: 104 | Iter: 000104 | Step Time: 15.299s | Local Loss: 0.0610 | Avg Loss: 0.2434 | LR: 0.000010
+[2026-01-11 19:33:02] Epoch: 105 | Iter: 000105 | Step Time: 16.349s | Local Loss: 0.1165 | Avg Loss: 0.2256 | LR: 0.000010
+[2026-01-11 19:33:24] Epoch: 106 | Iter: 000106 | Step Time: 16.255s | Local Loss: 0.2658 | Avg Loss: 0.2811 | LR: 0.000010
+[2026-01-11 19:33:45] Epoch: 107 | Iter: 000107 | Step Time: 14.951s | Local Loss: 0.0485 | Avg Loss: 0.2556 | LR: 0.000010
+[2026-01-11 19:34:06] Epoch: 108 | Iter: 000108 | Step Time: 14.938s | Local Loss: 0.5021 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-11 19:34:28] Epoch: 109 | Iter: 000109 | Step Time: 16.333s | Local Loss: 0.0875 | Avg Loss: 0.1715 | LR: 0.000010
+[2026-01-11 19:34:49] Epoch: 110 | Iter: 000110 | Step Time: 15.174s | Local Loss: 0.2177 | Avg Loss: 0.1081 | LR: 0.000010
+[2026-01-11 19:35:10] Epoch: 111 | Iter: 000111 | Step Time: 16.245s | Local Loss: 0.0670 | Avg Loss: 0.2630 | LR: 0.000010
+[2026-01-11 19:35:30] Epoch: 112 | Iter: 000112 | Step Time: 14.423s | Local Loss: 0.0198 | Avg Loss: 0.1654 | LR: 0.000010
+[2026-01-11 19:35:49] Epoch: 113 | Iter: 000113 | Step Time: 13.664s | Local Loss: 0.0394 | Avg Loss: 0.0881 | LR: 0.000010
+[2026-01-11 19:36:08] Epoch: 114 | Iter: 000114 | Step Time: 14.087s | Local Loss: 0.2429 | Avg Loss: 0.1940 | LR: 0.000010
+[2026-01-11 19:36:27] Epoch: 115 | Iter: 000115 | Step Time: 13.856s | Local Loss: 0.3679 | Avg Loss: 0.1095 | LR: 0.000010
+[2026-01-11 19:36:47] Epoch: 116 | Iter: 000116 | Step Time: 14.279s | Local Loss: 0.0788 | Avg Loss: 0.2649 | LR: 0.000010
+[2026-01-11 19:37:06] Epoch: 117 | Iter: 000117 | Step Time: 13.996s | Local Loss: 0.2829 | Avg Loss: 0.1553 | LR: 0.000010
+[2026-01-11 19:37:25] Epoch: 118 | Iter: 000118 | Step Time: 13.888s | Local Loss: 0.0188 | Avg Loss: 0.2031 | LR: 0.000010
+[2026-01-11 19:37:44] Epoch: 119 | Iter: 000119 | Step Time: 13.897s | Local Loss: 0.2410 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 19:38:03] Epoch: 120 | Iter: 000120 | Step Time: 13.479s | Local Loss: 0.0778 | Avg Loss: 0.1456 | LR: 0.000010
+[2026-01-11 19:38:22] Epoch: 121 | Iter: 000121 | Step Time: 13.989s | Local Loss: 0.0489 | Avg Loss: 0.1497 | LR: 0.000010
+[2026-01-11 19:38:41] Epoch: 122 | Iter: 000122 | Step Time: 13.877s | Local Loss: 0.0920 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-11 19:39:01] Epoch: 123 | Iter: 000123 | Step Time: 13.856s | Local Loss: 0.0513 | Avg Loss: 0.2624 | LR: 0.000010
+[2026-01-11 19:39:21] Epoch: 124 | Iter: 000124 | Step Time: 14.718s | Local Loss: 0.0987 | Avg Loss: 0.2069 | LR: 0.000010
+[2026-01-11 19:39:39] Epoch: 125 | Iter: 000125 | Step Time: 13.427s | Local Loss: 0.1305 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:39:58] Epoch: 126 | Iter: 000126 | Step Time: 13.992s | Local Loss: 0.1184 | Avg Loss: 0.0531 | LR: 0.000010
+[2026-01-11 19:40:18] Epoch: 127 | Iter: 000127 | Step Time: 13.955s | Local Loss: 0.4077 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-11 19:40:36] Epoch: 128 | Iter: 000128 | Step Time: 13.429s | Local Loss: 0.0637 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 19:40:56] Epoch: 129 | Iter: 000129 | Step Time: 14.960s | Local Loss: 0.0322 | Avg Loss: 0.1837 | LR: 0.000010
+[2026-01-11 19:41:15] Epoch: 130 | Iter: 000130 | Step Time: 13.708s | Local Loss: 0.3380 | Avg Loss: 0.0342 | LR: 0.000010
+[2026-01-11 19:41:35] Epoch: 131 | Iter: 000131 | Step Time: 14.310s | Local Loss: 0.0651 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-11 19:41:54] Epoch: 132 | Iter: 000132 | Step Time: 13.684s | Local Loss: 0.2892 | Avg Loss: 0.3134 | LR: 0.000010
+[2026-01-11 19:42:13] Epoch: 133 | Iter: 000133 | Step Time: 13.585s | Local Loss: 0.2382 | Avg Loss: 0.0917 | LR: 0.000010
+[2026-01-11 19:42:32] Epoch: 134 | Iter: 000134 | Step Time: 14.469s | Local Loss: 0.0860 | Avg Loss: 0.1496 | LR: 0.000010
+[2026-01-11 19:42:52] Epoch: 135 | Iter: 000135 | Step Time: 14.468s | Local Loss: 0.3045 | Avg Loss: 0.1055 | LR: 0.000010
+[2026-01-11 19:43:11] Epoch: 136 | Iter: 000136 | Step Time: 14.020s | Local Loss: 0.2065 | Avg Loss: 0.1378 | LR: 0.000010
+[2026-01-11 19:43:31] Epoch: 137 | Iter: 000137 | Step Time: 14.900s | Local Loss: 0.1902 | Avg Loss: 0.2133 | LR: 0.000010
+[2026-01-11 19:43:51] Epoch: 138 | Iter: 000138 | Step Time: 14.017s | Local Loss: 0.0912 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 19:44:11] Epoch: 139 | Iter: 000139 | Step Time: 14.723s | Local Loss: 0.0959 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-11 19:44:31] Epoch: 140 | Iter: 000140 | Step Time: 14.533s | Local Loss: 0.5060 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-11 19:44:51] Epoch: 141 | Iter: 000141 | Step Time: 14.847s | Local Loss: 0.0442 | Avg Loss: 0.1000 | LR: 0.000010
+[2026-01-11 19:45:10] Epoch: 142 | Iter: 000142 | Step Time: 13.616s | Local Loss: 0.0131 | Avg Loss: 0.0591 | LR: 0.000010
+[2026-01-11 19:45:29] Epoch: 143 | Iter: 000143 | Step Time: 13.981s | Local Loss: 0.1995 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-11 19:45:48] Epoch: 144 | Iter: 000144 | Step Time: 13.828s | Local Loss: 0.1874 | Avg Loss: 0.1651 | LR: 0.000010
+[2026-01-11 19:46:07] Epoch: 145 | Iter: 000145 | Step Time: 14.248s | Local Loss: 0.1103 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-11 19:46:26] Epoch: 146 | Iter: 000146 | Step Time: 13.797s | Local Loss: 0.0741 | Avg Loss: 0.1160 | LR: 0.000010
+[2026-01-11 19:46:46] Epoch: 147 | Iter: 000147 | Step Time: 14.129s | Local Loss: 0.0000 | Avg Loss: 0.1223 | LR: 0.000010
+[2026-01-11 19:47:05] Epoch: 148 | Iter: 000148 | Step Time: 14.366s | Local Loss: 0.0420 | Avg Loss: 0.1285 | LR: 0.000010
+[2026-01-11 19:47:25] Epoch: 149 | Iter: 000149 | Step Time: 13.906s | Local Loss: 0.3748 | Avg Loss: 0.0694 | LR: 0.000010
+[2026-01-11 19:50:25] Epoch: 150 | Iter: 000150 | Step Time: 22.159s | Local Loss: 0.1301 | Avg Loss: 0.1774 | LR: 0.000010
+[2026-01-11 19:50:49] Epoch: 151 | Iter: 000151 | Step Time: 16.983s | Local Loss: 0.0343 | Avg Loss: 0.1882 | LR: 0.000010
+[2026-01-11 19:51:13] Epoch: 152 | Iter: 000152 | Step Time: 17.789s | Local Loss: 0.0209 | Avg Loss: 0.0636 | LR: 0.000010
+[2026-01-11 19:51:37] Epoch: 153 | Iter: 000153 | Step Time: 18.269s | Local Loss: 0.0820 | Avg Loss: 0.0778 | LR: 0.000010
+[2026-01-11 19:52:02] Epoch: 154 | Iter: 000154 | Step Time: 18.514s | Local Loss: 0.0494 | Avg Loss: 0.0736 | LR: 0.000010
+[2026-01-11 19:52:26] Epoch: 155 | Iter: 000155 | Step Time: 17.369s | Local Loss: 0.4806 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-11 19:52:49] Epoch: 156 | Iter: 000156 | Step Time: 17.522s | Local Loss: 0.1692 | Avg Loss: 0.0985 | LR: 0.000010
+[2026-01-11 19:53:14] Epoch: 157 | Iter: 000157 | Step Time: 18.394s | Local Loss: 0.3126 | Avg Loss: 0.2140 | LR: 0.000010
+[2026-01-11 19:53:39] Epoch: 158 | Iter: 000158 | Step Time: 18.032s | Local Loss: 0.0238 | Avg Loss: 0.0962 | LR: 0.000010
+[2026-01-11 19:54:02] Epoch: 159 | Iter: 000159 | Step Time: 17.348s | Local Loss: 0.1688 | Avg Loss: 0.1300 | LR: 0.000010
+[2026-01-11 19:54:25] Epoch: 160 | Iter: 000160 | Step Time: 17.061s | Local Loss: 0.0422 | Avg Loss: 0.1764 | LR: 0.000010
+[2026-01-11 19:54:49] Epoch: 161 | Iter: 000161 | Step Time: 16.753s | Local Loss: 0.0926 | Avg Loss: 0.2164 | LR: 0.000010
+[2026-01-11 19:55:13] Epoch: 162 | Iter: 000162 | Step Time: 17.846s | Local Loss: 0.1121 | Avg Loss: 0.1013 | LR: 0.000010
+[2026-01-11 19:55:37] Epoch: 163 | Iter: 000163 | Step Time: 17.699s | Local Loss: 0.0572 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-11 19:56:00] Epoch: 164 | Iter: 000164 | Step Time: 17.395s | Local Loss: 0.0747 | Avg Loss: 0.0910 | LR: 0.000010
+[2026-01-11 19:56:23] Epoch: 165 | Iter: 000165 | Step Time: 16.984s | Local Loss: 0.0471 | Avg Loss: 0.1583 | LR: 0.000010
+[2026-01-11 19:56:47] Epoch: 166 | Iter: 000166 | Step Time: 17.435s | Local Loss: 0.0119 | Avg Loss: 0.0919 | LR: 0.000010
+[2026-01-11 19:57:12] Epoch: 167 | Iter: 000167 | Step Time: 18.584s | Local Loss: 0.0846 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-11 19:57:32] Epoch: 168 | Iter: 000168 | Step Time: 14.833s | Local Loss: 0.0191 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 19:57:51] Epoch: 169 | Iter: 000169 | Step Time: 14.003s | Local Loss: 0.0258 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-11 19:58:12] Epoch: 170 | Iter: 000170 | Step Time: 14.972s | Local Loss: 0.1564 | Avg Loss: 0.0879 | LR: 0.000010
+[2026-01-11 19:58:31] Epoch: 171 | Iter: 000171 | Step Time: 14.420s | Local Loss: 0.1055 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 19:58:50] Epoch: 172 | Iter: 000172 | Step Time: 13.554s | Local Loss: 0.0442 | Avg Loss: 0.2066 | LR: 0.000010
+[2026-01-11 19:59:10] Epoch: 173 | Iter: 000173 | Step Time: 14.407s | Local Loss: 0.0359 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 19:59:30] Epoch: 174 | Iter: 000174 | Step Time: 14.510s | Local Loss: 0.0969 | Avg Loss: 0.1445 | LR: 0.000010
+[2026-01-11 19:59:49] Epoch: 175 | Iter: 000175 | Step Time: 13.685s | Local Loss: 0.1028 | Avg Loss: 0.1062 | LR: 0.000010
+[2026-01-11 20:00:08] Epoch: 176 | Iter: 000176 | Step Time: 13.955s | Local Loss: 0.0170 | Avg Loss: 0.1344 | LR: 0.000010
+[2026-01-11 20:00:27] Epoch: 177 | Iter: 000177 | Step Time: 14.012s | Local Loss: 0.0173 | Avg Loss: 0.0708 | LR: 0.000010
+[2026-01-11 20:00:48] Epoch: 178 | Iter: 000178 | Step Time: 15.480s | Local Loss: 0.1421 | Avg Loss: 0.1590 | LR: 0.000010
+[2026-01-11 20:01:09] Epoch: 179 | Iter: 000179 | Step Time: 16.274s | Local Loss: 0.0408 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-11 20:01:29] Epoch: 180 | Iter: 000180 | Step Time: 14.264s | Local Loss: 0.0304 | Avg Loss: 0.0357 | LR: 0.000010
+[2026-01-11 20:01:49] Epoch: 181 | Iter: 000181 | Step Time: 14.879s | Local Loss: 0.0455 | Avg Loss: 0.0715 | LR: 0.000010
+[2026-01-11 20:02:09] Epoch: 182 | Iter: 000182 | Step Time: 14.492s | Local Loss: 0.0664 | Avg Loss: 0.1277 | LR: 0.000010
+[2026-01-11 20:02:29] Epoch: 183 | Iter: 000183 | Step Time: 14.404s | Local Loss: 0.0306 | Avg Loss: 0.1329 | LR: 0.000010
+[2026-01-11 20:02:48] Epoch: 184 | Iter: 000184 | Step Time: 14.371s | Local Loss: 0.2391 | Avg Loss: 0.2188 | LR: 0.000010
+[2026-01-11 20:03:08] Epoch: 185 | Iter: 000185 | Step Time: 14.414s | Local Loss: 0.0233 | Avg Loss: 0.1595 | LR: 0.000010
+[2026-01-11 20:03:27] Epoch: 186 | Iter: 000186 | Step Time: 14.247s | Local Loss: 0.1376 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:03:47] Epoch: 187 | Iter: 000187 | Step Time: 14.304s | Local Loss: 0.0146 | Avg Loss: 0.1886 | LR: 0.000010
+[2026-01-11 20:04:06] Epoch: 188 | Iter: 000188 | Step Time: 14.311s | Local Loss: 0.3136 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:04:26] Epoch: 189 | Iter: 000189 | Step Time: 14.116s | Local Loss: 0.0595 | Avg Loss: 0.1268 | LR: 0.000010
+[2026-01-11 20:04:45] Epoch: 190 | Iter: 000190 | Step Time: 13.990s | Local Loss: 0.1177 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:05:05] Epoch: 191 | Iter: 000191 | Step Time: 14.016s | Local Loss: 0.0267 | Avg Loss: 0.2208 | LR: 0.000010
+[2026-01-11 20:05:24] Epoch: 192 | Iter: 000192 | Step Time: 14.307s | Local Loss: 0.0415 | Avg Loss: 0.0602 | LR: 0.000010
+[2026-01-11 20:05:46] Epoch: 193 | Iter: 000193 | Step Time: 17.000s | Local Loss: 0.0340 | Avg Loss: 0.1887 | LR: 0.000010
+[2026-01-11 20:06:10] Epoch: 194 | Iter: 000194 | Step Time: 17.343s | Local Loss: 0.0689 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-11 20:06:32] Epoch: 195 | Iter: 000195 | Step Time: 15.987s | Local Loss: 0.2597 | Avg Loss: 0.1399 | LR: 0.000010
+[2026-01-11 20:06:53] Epoch: 196 | Iter: 000196 | Step Time: 15.740s | Local Loss: 0.0696 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:07:15] Epoch: 197 | Iter: 000197 | Step Time: 16.179s | Local Loss: 0.1016 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-11 20:07:34] Epoch: 198 | Iter: 000198 | Step Time: 14.138s | Local Loss: 0.0668 | Avg Loss: 0.0333 | LR: 0.000010
+[2026-01-11 20:07:54] Epoch: 199 | Iter: 000199 | Step Time: 14.515s | Local Loss: 0.0659 | Avg Loss: 0.1303 | LR: 0.000010
+[2026-01-11 20:11:01] Epoch: 200 | Iter: 000200 | Step Time: 22.229s | Local Loss: 0.0465 | Avg Loss: 0.0368 | LR: 0.000010
+[2026-01-11 20:11:25] Epoch: 201 | Iter: 000201 | Step Time: 17.398s | Local Loss: 0.0298 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-11 20:11:48] Epoch: 202 | Iter: 000202 | Step Time: 16.621s | Local Loss: 0.0188 | Avg Loss: 0.1003 | LR: 0.000010
+[2026-01-11 20:12:13] Epoch: 203 | Iter: 000203 | Step Time: 18.469s | Local Loss: 0.4027 | Avg Loss: 0.0555 | LR: 0.000010
+[2026-01-11 20:12:33] Epoch: 204 | Iter: 000204 | Step Time: 14.304s | Local Loss: 0.0328 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-11 20:12:53] Epoch: 205 | Iter: 000205 | Step Time: 14.042s | Local Loss: 0.0246 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 20:13:12] Epoch: 206 | Iter: 000206 | Step Time: 13.596s | Local Loss: 0.0507 | Avg Loss: 0.0404 | LR: 0.000010
+[2026-01-11 20:13:32] Epoch: 207 | Iter: 000207 | Step Time: 14.790s | Local Loss: 0.0181 | Avg Loss: 0.1577 | LR: 0.000010
+[2026-01-11 20:13:52] Epoch: 208 | Iter: 000208 | Step Time: 13.903s | Local Loss: 0.0461 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-11 20:14:11] Epoch: 209 | Iter: 000209 | Step Time: 13.609s | Local Loss: 0.0256 | Avg Loss: 0.0288 | LR: 0.000010
+[2026-01-11 20:14:30] Epoch: 210 | Iter: 000210 | Step Time: 13.834s | Local Loss: 0.0285 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-11 20:14:58] Epoch: 211 | Iter: 000211 | Step Time: 22.806s | Local Loss: 0.1316 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:15:25] Epoch: 212 | Iter: 000212 | Step Time: 20.276s | Local Loss: 0.0193 | Avg Loss: 0.0700 | LR: 0.000010
+[2026-01-11 20:15:45] Epoch: 213 | Iter: 000213 | Step Time: 14.366s | Local Loss: 0.0368 | Avg Loss: 0.0370 | LR: 0.000010
+[2026-01-11 20:16:05] Epoch: 214 | Iter: 000214 | Step Time: 14.278s | Local Loss: 0.3004 | Avg Loss: 0.1523 | LR: 0.000010
+[2026-01-11 20:16:27] Epoch: 215 | Iter: 000215 | Step Time: 17.016s | Local Loss: 0.0242 | Avg Loss: 0.0389 | LR: 0.000010
+[2026-01-11 20:16:47] Epoch: 216 | Iter: 000216 | Step Time: 14.222s | Local Loss: 0.0547 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-11 20:17:08] Epoch: 217 | Iter: 000217 | Step Time: 15.402s | Local Loss: 0.2699 | Avg Loss: 0.0516 | LR: 0.000010
+[2026-01-11 20:17:28] Epoch: 218 | Iter: 000218 | Step Time: 14.146s | Local Loss: 0.0231 | Avg Loss: 0.0701 | LR: 0.000010
+[2026-01-11 20:17:49] Epoch: 219 | Iter: 000219 | Step Time: 15.575s | Local Loss: 0.0679 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-11 20:18:09] Epoch: 220 | Iter: 000220 | Step Time: 13.660s | Local Loss: 0.4130 | Avg Loss: 0.0630 | LR: 0.000010
+[2026-01-11 20:18:28] Epoch: 221 | Iter: 000221 | Step Time: 13.966s | Local Loss: 0.0390 | Avg Loss: 0.1337 | LR: 0.000010
+[2026-01-11 20:18:48] Epoch: 222 | Iter: 000222 | Step Time: 14.095s | Local Loss: 0.0692 | Avg Loss: 0.0883 | LR: 0.000010
+[2026-01-11 20:19:08] Epoch: 223 | Iter: 000223 | Step Time: 14.090s | Local Loss: 0.2274 | Avg Loss: 0.1572 | LR: 0.000010
+[2026-01-11 20:19:29] Epoch: 224 | Iter: 000224 | Step Time: 15.092s | Local Loss: 0.2074 | Avg Loss: 0.0311 | LR: 0.000010
+[2026-01-11 20:19:48] Epoch: 225 | Iter: 000225 | Step Time: 13.949s | Local Loss: 0.0933 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-11 20:20:08] Epoch: 226 | Iter: 000226 | Step Time: 13.734s | Local Loss: 0.1784 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:20:27] Epoch: 227 | Iter: 000227 | Step Time: 13.816s | Local Loss: 0.0170 | Avg Loss: 0.1443 | LR: 0.000010
+[2026-01-11 20:20:47] Epoch: 228 | Iter: 000228 | Step Time: 14.401s | Local Loss: 0.0411 | Avg Loss: 0.0892 | LR: 0.000010
+[2026-01-11 20:21:08] Epoch: 229 | Iter: 000229 | Step Time: 14.893s | Local Loss: 0.0205 | Avg Loss: 0.1499 | LR: 0.000010
+[2026-01-11 20:21:28] Epoch: 230 | Iter: 000230 | Step Time: 14.191s | Local Loss: 0.0628 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 20:21:48] Epoch: 231 | Iter: 000231 | Step Time: 14.693s | Local Loss: 0.0225 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-11 20:22:07] Epoch: 232 | Iter: 000232 | Step Time: 13.515s | Local Loss: 0.0246 | Avg Loss: 0.2404 | LR: 0.000010
+[2026-01-11 20:22:27] Epoch: 233 | Iter: 000233 | Step Time: 14.125s | Local Loss: 0.0270 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-11 20:22:47] Epoch: 234 | Iter: 000234 | Step Time: 13.885s | Local Loss: 0.0259 | Avg Loss: 0.0943 | LR: 0.000010
+[2026-01-11 20:23:06] Epoch: 235 | Iter: 000235 | Step Time: 14.016s | Local Loss: 0.0224 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 20:23:27] Epoch: 236 | Iter: 000236 | Step Time: 14.734s | Local Loss: 0.1966 | Avg Loss: 0.0279 | LR: 0.000010
+[2026-01-11 20:23:46] Epoch: 237 | Iter: 000237 | Step Time: 13.612s | Local Loss: 0.0086 | Avg Loss: 0.1579 | LR: 0.000010
+[2026-01-11 20:24:06] Epoch: 238 | Iter: 000238 | Step Time: 14.598s | Local Loss: 0.0299 | Avg Loss: 0.1594 | LR: 0.000010
+[2026-01-11 20:24:26] Epoch: 239 | Iter: 000239 | Step Time: 14.297s | Local Loss: 0.0866 | Avg Loss: 0.0714 | LR: 0.000010
+[2026-01-11 20:24:46] Epoch: 240 | Iter: 000240 | Step Time: 14.218s | Local Loss: 0.0774 | Avg Loss: 0.0811 | LR: 0.000010
+[2026-01-11 20:25:06] Epoch: 241 | Iter: 000241 | Step Time: 14.757s | Local Loss: 0.1027 | Avg Loss: 0.1113 | LR: 0.000010
+[2026-01-11 20:25:26] Epoch: 242 | Iter: 000242 | Step Time: 13.716s | Local Loss: 0.0668 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 20:25:45] Epoch: 243 | Iter: 000243 | Step Time: 14.107s | Local Loss: 0.0840 | Avg Loss: 0.1089 | LR: 0.000010
+[2026-01-11 20:26:06] Epoch: 244 | Iter: 000244 | Step Time: 14.758s | Local Loss: 0.0528 | Avg Loss: 0.1037 | LR: 0.000010
+[2026-01-11 20:26:26] Epoch: 245 | Iter: 000245 | Step Time: 14.215s | Local Loss: 0.0249 | Avg Loss: 0.1166 | LR: 0.000010
+[2026-01-11 20:26:46] Epoch: 246 | Iter: 000246 | Step Time: 14.901s | Local Loss: 0.1970 | Avg Loss: 0.0902 | LR: 0.000010
+[2026-01-11 20:27:05] Epoch: 247 | Iter: 000247 | Step Time: 13.533s | Local Loss: 0.0190 | Avg Loss: 0.1098 | LR: 0.000010
+[2026-01-11 20:27:26] Epoch: 248 | Iter: 000248 | Step Time: 14.887s | Local Loss: 0.2545 | Avg Loss: 0.0599 | LR: 0.000010
+[2026-01-11 20:27:46] Epoch: 249 | Iter: 000249 | Step Time: 14.302s | Local Loss: 0.0227 | Avg Loss: 0.1159 | LR: 0.000010
+[2026-01-11 20:30:52] Epoch: 250 | Iter: 000250 | Step Time: 21.968s | Local Loss: 0.1516 | Avg Loss: 0.0480 | LR: 0.000010
+[2026-01-11 20:31:14] Epoch: 251 | Iter: 000251 | Step Time: 16.070s | Local Loss: 0.0275 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-11 20:31:35] Epoch: 252 | Iter: 000252 | Step Time: 15.024s | Local Loss: 0.0643 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-11 20:31:58] Epoch: 253 | Iter: 000253 | Step Time: 17.077s | Local Loss: 0.0481 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:32:19] Epoch: 254 | Iter: 000254 | Step Time: 14.611s | Local Loss: 0.0213 | Avg Loss: 0.1469 | LR: 0.000010
+[2026-01-11 20:32:41] Epoch: 255 | Iter: 000255 | Step Time: 15.427s | Local Loss: 0.0230 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 20:33:02] Epoch: 256 | Iter: 000256 | Step Time: 14.907s | Local Loss: 0.0546 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 20:33:24] Epoch: 257 | Iter: 000257 | Step Time: 16.014s | Local Loss: 0.0502 | Avg Loss: 0.0955 | LR: 0.000010
+[2026-01-11 20:33:46] Epoch: 258 | Iter: 000258 | Step Time: 16.263s | Local Loss: 0.1871 | Avg Loss: 0.1868 | LR: 0.000010
+[2026-01-11 20:34:05] Epoch: 259 | Iter: 000259 | Step Time: 13.275s | Local Loss: 0.0124 | Avg Loss: 0.0518 | LR: 0.000010
+[2026-01-11 20:34:26] Epoch: 260 | Iter: 000260 | Step Time: 15.017s | Local Loss: 0.0450 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-11 20:34:45] Epoch: 261 | Iter: 000261 | Step Time: 13.463s | Local Loss: 0.0176 | Avg Loss: 0.0431 | LR: 0.000010
+[2026-01-11 20:35:04] Epoch: 262 | Iter: 000262 | Step Time: 13.390s | Local Loss: 0.0344 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-11 20:35:25] Epoch: 263 | Iter: 000263 | Step Time: 14.898s | Local Loss: 0.1134 | Avg Loss: 0.0374 | LR: 0.000010
+[2026-01-11 20:35:44] Epoch: 264 | Iter: 000264 | Step Time: 13.302s | Local Loss: 0.1501 | Avg Loss: 0.1468 | LR: 0.000010
+[2026-01-11 20:36:04] Epoch: 265 | Iter: 000265 | Step Time: 14.593s | Local Loss: 0.0408 | Avg Loss: 0.0775 | LR: 0.000010
+[2026-01-11 20:36:23] Epoch: 266 | Iter: 000266 | Step Time: 13.923s | Local Loss: 0.0708 | Avg Loss: 0.0656 | LR: 0.000010
+[2026-01-11 20:36:42] Epoch: 267 | Iter: 000267 | Step Time: 13.511s | Local Loss: 0.0447 | Avg Loss: 0.1406 | LR: 0.000010
+[2026-01-11 20:37:03] Epoch: 268 | Iter: 000268 | Step Time: 15.093s | Local Loss: 0.0393 | Avg Loss: 0.0745 | LR: 0.000010
+[2026-01-11 20:37:22] Epoch: 269 | Iter: 000269 | Step Time: 13.513s | Local Loss: 0.0341 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 20:37:41] Epoch: 270 | Iter: 000270 | Step Time: 13.565s | Local Loss: 0.0182 | Avg Loss: 0.0254 | LR: 0.000010
+[2026-01-11 20:38:01] Epoch: 271 | Iter: 000271 | Step Time: 14.267s | Local Loss: 0.0205 | Avg Loss: 0.0360 | LR: 0.000010
+[2026-01-11 20:38:20] Epoch: 272 | Iter: 000272 | Step Time: 13.765s | Local Loss: 0.0265 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-11 20:38:40] Epoch: 273 | Iter: 000273 | Step Time: 14.380s | Local Loss: 0.0269 | Avg Loss: 0.0565 | LR: 0.000010
+[2026-01-11 20:38:59] Epoch: 274 | Iter: 000274 | Step Time: 13.678s | Local Loss: 0.0776 | Avg Loss: 0.1074 | LR: 0.000010
+[2026-01-11 20:39:19] Epoch: 275 | Iter: 000275 | Step Time: 13.641s | Local Loss: 0.0331 | Avg Loss: 0.0458 | LR: 0.000010
+[2026-01-11 20:39:39] Epoch: 276 | Iter: 000276 | Step Time: 14.842s | Local Loss: 0.0586 | Avg Loss: 0.0452 | LR: 0.000010
+[2026-01-11 20:39:58] Epoch: 277 | Iter: 000277 | Step Time: 13.653s | Local Loss: 0.0430 | Avg Loss: 0.0660 | LR: 0.000010
+[2026-01-11 20:40:19] Epoch: 278 | Iter: 000278 | Step Time: 14.847s | Local Loss: 0.0234 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-11 20:40:39] Epoch: 279 | Iter: 000279 | Step Time: 14.317s | Local Loss: 0.0220 | Avg Loss: 0.1431 | LR: 0.000010
+[2026-01-11 20:40:58] Epoch: 280 | Iter: 000280 | Step Time: 13.719s | Local Loss: 0.1252 | Avg Loss: 0.0489 | LR: 0.000010
+[2026-01-11 20:41:19] Epoch: 281 | Iter: 000281 | Step Time: 15.465s | Local Loss: 0.0166 | Avg Loss: 0.0154 | LR: 0.000010
+[2026-01-11 20:41:38] Epoch: 282 | Iter: 000282 | Step Time: 13.546s | Local Loss: 0.0350 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-11 20:41:58] Epoch: 283 | Iter: 000283 | Step Time: 13.966s | Local Loss: 0.0198 | Avg Loss: 0.0326 | LR: 0.000010
+[2026-01-11 20:42:19] Epoch: 284 | Iter: 000284 | Step Time: 15.771s | Local Loss: 0.0675 | Avg Loss: 0.1155 | LR: 0.000010
+[2026-01-11 20:42:38] Epoch: 285 | Iter: 000285 | Step Time: 13.203s | Local Loss: 0.0121 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-11 20:42:58] Epoch: 286 | Iter: 000286 | Step Time: 14.206s | Local Loss: 0.0072 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-11 20:43:18] Epoch: 287 | Iter: 000287 | Step Time: 14.176s | Local Loss: 0.0161 | Avg Loss: 0.1375 | LR: 0.000010
+[2026-01-11 20:43:37] Epoch: 288 | Iter: 000288 | Step Time: 14.024s | Local Loss: 0.0536 | Avg Loss: 0.1215 | LR: 0.000010
+[2026-01-11 20:43:57] Epoch: 289 | Iter: 000289 | Step Time: 14.122s | Local Loss: 0.0648 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-11 20:44:16] Epoch: 290 | Iter: 000290 | Step Time: 13.600s | Local Loss: 0.0149 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-11 20:44:36] Epoch: 291 | Iter: 000291 | Step Time: 14.263s | Local Loss: 0.2160 | Avg Loss: 0.1138 | LR: 0.000010
+[2026-01-11 20:44:56] Epoch: 292 | Iter: 000292 | Step Time: 14.639s | Local Loss: 0.0351 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 20:45:16] Epoch: 293 | Iter: 000293 | Step Time: 14.045s | Local Loss: 0.0444 | Avg Loss: 0.1361 | LR: 0.000010
+[2026-01-11 20:45:35] Epoch: 294 | Iter: 000294 | Step Time: 13.909s | Local Loss: 0.0575 | Avg Loss: 0.1673 | LR: 0.000010
+[2026-01-11 20:45:55] Epoch: 295 | Iter: 000295 | Step Time: 13.558s | Local Loss: 0.0366 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 20:46:14] Epoch: 296 | Iter: 000296 | Step Time: 13.766s | Local Loss: 0.0703 | Avg Loss: 0.0825 | LR: 0.000010
+[2026-01-11 20:46:34] Epoch: 297 | Iter: 000297 | Step Time: 14.611s | Local Loss: 0.0769 | Avg Loss: 0.1343 | LR: 0.000010
+[2026-01-11 20:46:54] Epoch: 298 | Iter: 000298 | Step Time: 14.230s | Local Loss: 0.1493 | Avg Loss: 0.1126 | LR: 0.000010
+[2026-01-11 20:47:14] Epoch: 299 | Iter: 000299 | Step Time: 14.321s | Local Loss: 0.0214 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 20:50:22] Epoch: 300 | Iter: 000300 | Step Time: 24.536s | Local Loss: 0.0179 | Avg Loss: 0.1024 | LR: 0.000010
+[2026-01-11 20:50:48] Epoch: 301 | Iter: 000301 | Step Time: 19.200s | Local Loss: 0.0385 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:51:12] Epoch: 302 | Iter: 000302 | Step Time: 17.494s | Local Loss: 0.0438 | Avg Loss: 0.1770 | LR: 0.000010
+[2026-01-11 20:51:38] Epoch: 303 | Iter: 000303 | Step Time: 19.347s | Local Loss: 0.0339 | Avg Loss: 0.0445 | LR: 0.000010
+[2026-01-11 20:52:03] Epoch: 304 | Iter: 000304 | Step Time: 18.961s | Local Loss: 0.0173 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-11 20:52:30] Epoch: 305 | Iter: 000305 | Step Time: 20.718s | Local Loss: 0.0120 | Avg Loss: 0.0672 | LR: 0.000010
+[2026-01-11 20:52:55] Epoch: 306 | Iter: 000306 | Step Time: 17.503s | Local Loss: 0.0039 | Avg Loss: 0.0112 | LR: 0.000010
+[2026-01-11 20:53:20] Epoch: 307 | Iter: 000307 | Step Time: 18.997s | Local Loss: 0.0112 | Avg Loss: 0.0583 | LR: 0.000010
+[2026-01-11 20:53:45] Epoch: 308 | Iter: 000308 | Step Time: 17.466s | Local Loss: 0.2705 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-11 20:54:10] Epoch: 309 | Iter: 000309 | Step Time: 18.640s | Local Loss: 0.3120 | Avg Loss: 0.1627 | LR: 0.000010
+[2026-01-11 20:54:34] Epoch: 310 | Iter: 000310 | Step Time: 17.557s | Local Loss: 0.0000 | Avg Loss: 0.0527 | LR: 0.000010
+[2026-01-11 20:54:58] Epoch: 311 | Iter: 000311 | Step Time: 17.314s | Local Loss: 0.0456 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 20:55:23] Epoch: 312 | Iter: 000312 | Step Time: 17.525s | Local Loss: 0.0259 | Avg Loss: 0.0590 | LR: 0.000010
+[2026-01-11 20:55:47] Epoch: 313 | Iter: 000313 | Step Time: 17.118s | Local Loss: 0.0085 | Avg Loss: 0.0299 | LR: 0.000010
+[2026-01-11 20:56:12] Epoch: 314 | Iter: 000314 | Step Time: 18.471s | Local Loss: 0.0226 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-11 20:56:31] Epoch: 315 | Iter: 000315 | Step Time: 13.474s | Local Loss: 0.1032 | Avg Loss: 0.0849 | LR: 0.000010
+[2026-01-11 20:56:51] Epoch: 316 | Iter: 000316 | Step Time: 13.838s | Local Loss: 0.2429 | Avg Loss: 0.0348 | LR: 0.000010
+[2026-01-11 20:57:11] Epoch: 317 | Iter: 000317 | Step Time: 14.229s | Local Loss: 0.0669 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-11 20:57:31] Epoch: 318 | Iter: 000318 | Step Time: 14.623s | Local Loss: 0.0128 | Avg Loss: 0.0261 | LR: 0.000010
+[2026-01-11 20:57:51] Epoch: 319 | Iter: 000319 | Step Time: 14.280s | Local Loss: 0.0297 | Avg Loss: 0.1088 | LR: 0.000010
+[2026-01-11 20:58:10] Epoch: 320 | Iter: 000320 | Step Time: 13.560s | Local Loss: 0.0258 | Avg Loss: 0.0568 | LR: 0.000010
+[2026-01-11 20:58:29] Epoch: 321 | Iter: 000321 | Step Time: 13.794s | Local Loss: 0.0274 | Avg Loss: 0.0354 | LR: 0.000010
+[2026-01-11 20:58:49] Epoch: 322 | Iter: 000322 | Step Time: 14.331s | Local Loss: 0.0110 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-11 20:59:11] Epoch: 323 | Iter: 000323 | Step Time: 16.920s | Local Loss: 0.2813 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-11 20:59:33] Epoch: 324 | Iter: 000324 | Step Time: 15.880s | Local Loss: 0.0174 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 20:59:54] Epoch: 325 | Iter: 000325 | Step Time: 15.475s | Local Loss: 0.0499 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:00:17] Epoch: 326 | Iter: 000326 | Step Time: 17.231s | Local Loss: 0.0077 | Avg Loss: 0.0541 | LR: 0.000010
+[2026-01-11 21:00:37] Epoch: 327 | Iter: 000327 | Step Time: 14.953s | Local Loss: 0.0882 | Avg Loss: 0.0322 | LR: 0.000010
+[2026-01-11 21:00:57] Epoch: 328 | Iter: 000328 | Step Time: 14.314s | Local Loss: 0.0162 | Avg Loss: 0.1391 | LR: 0.000010
+[2026-01-11 21:01:17] Epoch: 329 | Iter: 000329 | Step Time: 14.876s | Local Loss: 0.0440 | Avg Loss: 0.2372 | LR: 0.000010
+[2026-01-11 21:01:38] Epoch: 330 | Iter: 000330 | Step Time: 15.195s | Local Loss: 0.0175 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:01:59] Epoch: 331 | Iter: 000331 | Step Time: 15.313s | Local Loss: 0.0584 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 21:02:19] Epoch: 332 | Iter: 000332 | Step Time: 14.950s | Local Loss: 0.0975 | Avg Loss: 0.0324 | LR: 0.000010
+[2026-01-11 21:02:39] Epoch: 333 | Iter: 000333 | Step Time: 13.856s | Local Loss: 0.0212 | Avg Loss: 0.0373 | LR: 0.000010
+[2026-01-11 21:03:00] Epoch: 334 | Iter: 000334 | Step Time: 15.323s | Local Loss: 0.0299 | Avg Loss: 0.0519 | LR: 0.000010
+[2026-01-11 21:03:19] Epoch: 335 | Iter: 000335 | Step Time: 14.085s | Local Loss: 0.0454 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-11 21:03:39] Epoch: 336 | Iter: 000336 | Step Time: 14.403s | Local Loss: 0.0259 | Avg Loss: 0.0477 | LR: 0.000010
+[2026-01-11 21:04:00] Epoch: 337 | Iter: 000337 | Step Time: 14.887s | Local Loss: 0.1232 | Avg Loss: 0.0675 | LR: 0.000010
+[2026-01-11 21:04:20] Epoch: 338 | Iter: 000338 | Step Time: 14.684s | Local Loss: 0.0370 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-11 21:04:42] Epoch: 339 | Iter: 000339 | Step Time: 16.564s | Local Loss: 0.0333 | Avg Loss: 0.0262 | LR: 0.000010
+[2026-01-11 21:05:04] Epoch: 340 | Iter: 000340 | Step Time: 16.030s | Local Loss: 0.0367 | Avg Loss: 0.0874 | LR: 0.000010
+[2026-01-11 21:05:25] Epoch: 341 | Iter: 000341 | Step Time: 15.575s | Local Loss: 0.0181 | Avg Loss: 0.0798 | LR: 0.000010
+[2026-01-11 21:05:45] Epoch: 342 | Iter: 000342 | Step Time: 14.866s | Local Loss: 0.0660 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-11 21:06:06] Epoch: 343 | Iter: 000343 | Step Time: 14.733s | Local Loss: 0.0434 | Avg Loss: 0.0627 | LR: 0.000010
+[2026-01-11 21:06:26] Epoch: 344 | Iter: 000344 | Step Time: 14.341s | Local Loss: 0.0165 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:06:45] Epoch: 345 | Iter: 000345 | Step Time: 13.911s | Local Loss: 0.0510 | Avg Loss: 0.0314 | LR: 0.000010
+[2026-01-11 21:07:05] Epoch: 346 | Iter: 000346 | Step Time: 14.427s | Local Loss: 0.0276 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-11 21:07:24] Epoch: 347 | Iter: 000347 | Step Time: 14.099s | Local Loss: 0.2006 | Avg Loss: 0.0260 | LR: 0.000010
+[2026-01-11 21:07:45] Epoch: 348 | Iter: 000348 | Step Time: 14.700s | Local Loss: 0.0431 | Avg Loss: 0.0205 | LR: 0.000010
+[2026-01-11 21:08:04] Epoch: 349 | Iter: 000349 | Step Time: 14.102s | Local Loss: 0.0221 | Avg Loss: 0.0645 | LR: 0.000010
+[2026-01-11 21:11:08] Epoch: 350 | Iter: 000350 | Step Time: 19.382s | Local Loss: 0.0219 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:11:31] Epoch: 351 | Iter: 000351 | Step Time: 16.997s | Local Loss: 0.2474 | Avg Loss: 0.0513 | LR: 0.000010
+[2026-01-11 21:11:54] Epoch: 352 | Iter: 000352 | Step Time: 17.847s | Local Loss: 0.0209 | Avg Loss: 0.1028 | LR: 0.000010
+[2026-01-11 21:12:17] Epoch: 353 | Iter: 000353 | Step Time: 16.926s | Local Loss: 0.0349 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:12:41] Epoch: 354 | Iter: 000354 | Step Time: 17.579s | Local Loss: 0.0540 | Avg Loss: 0.0188 | LR: 0.000010
+[2026-01-11 21:13:03] Epoch: 355 | Iter: 000355 | Step Time: 16.848s | Local Loss: 0.0144 | Avg Loss: 0.0487 | LR: 0.000010
+[2026-01-11 21:13:25] Epoch: 356 | Iter: 000356 | Step Time: 15.432s | Local Loss: 0.0107 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-11 21:13:48] Epoch: 357 | Iter: 000357 | Step Time: 16.862s | Local Loss: 0.0352 | Avg Loss: 0.1238 | LR: 0.000010
+[2026-01-11 21:14:08] Epoch: 358 | Iter: 000358 | Step Time: 14.952s | Local Loss: 0.2030 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-11 21:14:34] Epoch: 359 | Iter: 000359 | Step Time: 19.697s | Local Loss: 0.0151 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-11 21:15:04] Epoch: 360 | Iter: 000360 | Step Time: 24.562s | Local Loss: 0.0080 | Avg Loss: 0.0603 | LR: 0.000010
+[2026-01-11 21:15:27] Epoch: 361 | Iter: 000361 | Step Time: 16.510s | Local Loss: 0.0108 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:15:49] Epoch: 362 | Iter: 000362 | Step Time: 14.959s | Local Loss: 0.2224 | Avg Loss: 0.0408 | LR: 0.000010
+[2026-01-11 21:16:10] Epoch: 363 | Iter: 000363 | Step Time: 14.973s | Local Loss: 0.0172 | Avg Loss: 0.0511 | LR: 0.000010
+[2026-01-11 21:16:31] Epoch: 364 | Iter: 000364 | Step Time: 15.212s | Local Loss: 0.0359 | Avg Loss: 0.0380 | LR: 0.000010
+[2026-01-11 21:16:53] Epoch: 365 | Iter: 000365 | Step Time: 14.884s | Local Loss: 0.0300 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:17:14] Epoch: 366 | Iter: 000366 | Step Time: 15.401s | Local Loss: 0.0554 | Avg Loss: 0.0821 | LR: 0.000010
+[2026-01-11 21:17:36] Epoch: 367 | Iter: 000367 | Step Time: 15.384s | Local Loss: 0.0178 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-11 21:17:57] Epoch: 368 | Iter: 000368 | Step Time: 15.280s | Local Loss: 0.0191 | Avg Loss: 0.0183 | LR: 0.000010
+[2026-01-11 21:18:17] Epoch: 369 | Iter: 000369 | Step Time: 13.669s | Local Loss: 0.0381 | Avg Loss: 0.1248 | LR: 0.000010
+[2026-01-11 21:18:36] Epoch: 370 | Iter: 000370 | Step Time: 13.140s | Local Loss: 0.0239 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:18:54] Epoch: 371 | Iter: 000371 | Step Time: 12.980s | Local Loss: 0.0159 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:19:14] Epoch: 372 | Iter: 000372 | Step Time: 13.843s | Local Loss: 0.2343 | Avg Loss: 0.0257 | LR: 0.000010
+[2026-01-11 21:19:34] Epoch: 373 | Iter: 000373 | Step Time: 14.273s | Local Loss: 0.0156 | Avg Loss: 0.0162 | LR: 0.000010
+[2026-01-11 21:19:54] Epoch: 374 | Iter: 000374 | Step Time: 14.655s | Local Loss: 0.0178 | Avg Loss: 0.0845 | LR: 0.000010
+[2026-01-11 21:20:14] Epoch: 375 | Iter: 000375 | Step Time: 14.230s | Local Loss: 0.0187 | Avg Loss: 0.0770 | LR: 0.000010
+[2026-01-11 21:20:34] Epoch: 376 | Iter: 000376 | Step Time: 13.736s | Local Loss: 0.0000 | Avg Loss: 0.0240 | LR: 0.000010
+[2026-01-11 21:20:53] Epoch: 377 | Iter: 000377 | Step Time: 13.376s | Local Loss: 0.0127 | Avg Loss: 0.1051 | LR: 0.000010
+[2026-01-11 21:21:12] Epoch: 378 | Iter: 000378 | Step Time: 13.049s | Local Loss: 0.0266 | Avg Loss: 0.0263 | LR: 0.000010
+[2026-01-11 21:21:33] Epoch: 379 | Iter: 000379 | Step Time: 14.966s | Local Loss: 0.0172 | Avg Loss: 0.0148 | LR: 0.000010
+[2026-01-11 21:21:51] Epoch: 380 | Iter: 000380 | Step Time: 12.874s | Local Loss: 0.0128 | Avg Loss: 0.0100 | LR: 0.000010
+[2026-01-11 21:22:11] Epoch: 381 | Iter: 000381 | Step Time: 13.721s | Local Loss: 0.0726 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-11 21:22:30] Epoch: 382 | Iter: 000382 | Step Time: 13.349s | Local Loss: 0.0134 | Avg Loss: 0.0542 | LR: 0.000010
+[2026-01-11 21:22:49] Epoch: 383 | Iter: 000383 | Step Time: 13.318s | Local Loss: 0.0135 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-11 21:23:09] Epoch: 384 | Iter: 000384 | Step Time: 14.508s | Local Loss: 0.1652 | Avg Loss: 0.0418 | LR: 0.000010
+[2026-01-11 21:23:28] Epoch: 385 | Iter: 000385 | Step Time: 13.253s | Local Loss: 0.0777 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:23:47] Epoch: 386 | Iter: 000386 | Step Time: 12.916s | Local Loss: 0.0388 | Avg Loss: 0.0617 | LR: 0.000010
+[2026-01-11 21:24:07] Epoch: 387 | Iter: 000387 | Step Time: 14.519s | Local Loss: 0.1209 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 21:24:26] Epoch: 388 | Iter: 000388 | Step Time: 13.275s | Local Loss: 0.0259 | Avg Loss: 0.1038 | LR: 0.000010
+[2026-01-11 21:24:45] Epoch: 389 | Iter: 000389 | Step Time: 13.540s | Local Loss: 0.3030 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-11 21:25:05] Epoch: 390 | Iter: 000390 | Step Time: 13.668s | Local Loss: 0.0331 | Avg Loss: 0.0848 | LR: 0.000010
+[2026-01-11 21:25:24] Epoch: 391 | Iter: 000391 | Step Time: 13.173s | Local Loss: 0.0062 | Avg Loss: 0.0901 | LR: 0.000010
+[2026-01-11 21:25:44] Epoch: 392 | Iter: 000392 | Step Time: 14.001s | Local Loss: 0.0319 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-11 21:26:03] Epoch: 393 | Iter: 000393 | Step Time: 13.097s | Local Loss: 0.0088 | Avg Loss: 0.0951 | LR: 0.000010
+[2026-01-11 21:26:22] Epoch: 394 | Iter: 000394 | Step Time: 13.132s | Local Loss: 0.2075 | Avg Loss: 0.0956 | LR: 0.000010
+[2026-01-11 21:26:42] Epoch: 395 | Iter: 000395 | Step Time: 13.750s | Local Loss: 0.0205 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-11 21:27:01] Epoch: 396 | Iter: 000396 | Step Time: 13.090s | Local Loss: 0.0656 | Avg Loss: 0.0295 | LR: 0.000010
+[2026-01-11 21:27:21] Epoch: 397 | Iter: 000397 | Step Time: 14.146s | Local Loss: 0.0224 | Avg Loss: 0.0539 | LR: 0.000010
+[2026-01-11 21:27:39] Epoch: 398 | Iter: 000398 | Step Time: 12.981s | Local Loss: 0.1111 | Avg Loss: 0.0552 | LR: 0.000010
+[2026-01-11 21:27:58] Epoch: 399 | Iter: 000399 | Step Time: 13.248s | Local Loss: 0.0162 | Avg Loss: 0.0200 | LR: 0.000010
+[2026-01-11 21:31:02] Epoch: 400 | Iter: 000400 | Step Time: 21.733s | Local Loss: 0.0442 | Avg Loss: 0.0248 | LR: 0.000010
+[2026-01-11 21:31:27] Epoch: 401 | Iter: 000401 | Step Time: 17.353s | Local Loss: 0.0127 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-11 21:31:50] Epoch: 402 | Iter: 000402 | Step Time: 16.808s | Local Loss: 0.0425 | Avg Loss: 0.0536 | LR: 0.000010
+[2026-01-11 21:32:15] Epoch: 403 | Iter: 000403 | Step Time: 17.260s | Local Loss: 0.0266 | Avg Loss: 0.0702 | LR: 0.000010
+[2026-01-11 21:32:38] Epoch: 404 | Iter: 000404 | Step Time: 16.642s | Local Loss: 0.0083 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-11 21:33:03] Epoch: 405 | Iter: 000405 | Step Time: 17.558s | Local Loss: 0.0287 | Avg Loss: 0.0120 | LR: 0.000010
+[2026-01-11 21:33:28] Epoch: 406 | Iter: 000406 | Step Time: 18.223s | Local Loss: 0.0109 | Avg Loss: 0.0950 | LR: 0.000010
+[2026-01-11 21:33:48] Epoch: 407 | Iter: 000407 | Step Time: 14.518s | Local Loss: 0.0698 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-11 21:34:07] Epoch: 408 | Iter: 000408 | Step Time: 13.089s | Local Loss: 0.1200 | Avg Loss: 0.0259 | LR: 0.000010
+[2026-01-11 21:34:27] Epoch: 409 | Iter: 000409 | Step Time: 14.196s | Local Loss: 0.0218 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-11 21:34:46] Epoch: 410 | Iter: 000410 | Step Time: 13.472s | Local Loss: 0.2285 | Avg Loss: 0.0952 | LR: 0.000010
+[2026-01-11 21:35:05] Epoch: 411 | Iter: 000411 | Step Time: 13.293s | Local Loss: 0.0605 | Avg Loss: 0.0193 | LR: 0.000010
+[2026-01-11 21:35:24] Epoch: 412 | Iter: 000412 | Step Time: 13.221s | Local Loss: 0.0355 | Avg Loss: 0.0424 | LR: 0.000010
+[2026-01-11 21:35:44] Epoch: 413 | Iter: 000413 | Step Time: 13.981s | Local Loss: 0.0084 | Avg Loss: 0.0732 | LR: 0.000010
+[2026-01-11 21:36:03] Epoch: 414 | Iter: 000414 | Step Time: 13.897s | Local Loss: 0.1703 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:36:23] Epoch: 415 | Iter: 000415 | Step Time: 13.371s | Local Loss: 0.0304 | Avg Loss: 0.0318 | LR: 0.000010
+[2026-01-11 21:36:42] Epoch: 416 | Iter: 000416 | Step Time: 14.044s | Local Loss: 0.0128 | Avg Loss: 0.0521 | LR: 0.000010
+[2026-01-11 21:37:02] Epoch: 417 | Iter: 000417 | Step Time: 13.624s | Local Loss: 0.0065 | Avg Loss: 0.0806 | LR: 0.000010
+[2026-01-11 21:37:20] Epoch: 418 | Iter: 000418 | Step Time: 13.042s | Local Loss: 0.0266 | Avg Loss: 0.0176 | LR: 0.000010
+[2026-01-11 21:37:40] Epoch: 419 | Iter: 000419 | Step Time: 14.279s | Local Loss: 0.0141 | Avg Loss: 0.0613 | LR: 0.000010
+[2026-01-11 21:38:00] Epoch: 420 | Iter: 000420 | Step Time: 14.067s | Local Loss: 0.0271 | Avg Loss: 0.0436 | LR: 0.000010
+[2026-01-11 21:38:19] Epoch: 421 | Iter: 000421 | Step Time: 13.349s | Local Loss: 0.0310 | Avg Loss: 0.1281 | LR: 0.000010
+[2026-01-11 21:38:38] Epoch: 422 | Iter: 000422 | Step Time: 13.372s | Local Loss: 0.0056 | Avg Loss: 0.0171 | LR: 0.000010
+[2026-01-11 21:38:57] Epoch: 423 | Iter: 000423 | Step Time: 13.014s | Local Loss: 0.0124 | Avg Loss: 0.0350 | LR: 0.000010
+[2026-01-11 21:39:16] Epoch: 424 | Iter: 000424 | Step Time: 13.383s | Local Loss: 0.0167 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-11 21:39:36] Epoch: 425 | Iter: 000425 | Step Time: 13.527s | Local Loss: 0.1319 | Avg Loss: 0.0117 | LR: 0.000010
+[2026-01-11 21:39:54] Epoch: 426 | Iter: 000426 | Step Time: 13.019s | Local Loss: 0.0117 | Avg Loss: 0.0212 | LR: 0.000010
+[2026-01-11 21:40:13] Epoch: 427 | Iter: 000427 | Step Time: 13.300s | Local Loss: 0.0173 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-11 21:40:33] Epoch: 428 | Iter: 000428 | Step Time: 13.490s | Local Loss: 0.1822 | Avg Loss: 0.0124 | LR: 0.000010
+[2026-01-11 21:40:52] Epoch: 429 | Iter: 000429 | Step Time: 13.841s | Local Loss: 0.0092 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:41:12] Epoch: 430 | Iter: 000430 | Step Time: 13.575s | Local Loss: 0.0176 | Avg Loss: 0.0208 | LR: 0.000010
+[2026-01-11 21:41:31] Epoch: 431 | Iter: 000431 | Step Time: 13.581s | Local Loss: 0.0226 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-11 21:41:51] Epoch: 432 | Iter: 000432 | Step Time: 14.090s | Local Loss: 0.0208 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-11 21:42:10] Epoch: 433 | Iter: 000433 | Step Time: 13.449s | Local Loss: 0.3565 | Avg Loss: 0.0203 | LR: 0.000010
+[2026-01-11 21:42:30] Epoch: 434 | Iter: 000434 | Step Time: 14.485s | Local Loss: 0.0209 | Avg Loss: 0.0168 | LR: 0.000010
+[2026-01-11 21:42:49] Epoch: 435 | Iter: 000435 | Step Time: 13.039s | Local Loss: 0.0484 | Avg Loss: 0.0695 | LR: 0.000010
+[2026-01-11 21:43:09] Epoch: 436 | Iter: 000436 | Step Time: 13.543s | Local Loss: 0.0156 | Avg Loss: 0.0107 | LR: 0.000010
+[2026-01-11 21:43:28] Epoch: 437 | Iter: 000437 | Step Time: 13.472s | Local Loss: 0.1325 | Avg Loss: 0.0144 | LR: 0.000010
+[2026-01-11 21:43:47] Epoch: 438 | Iter: 000438 | Step Time: 13.328s | Local Loss: 0.0030 | Avg Loss: 0.1033 | LR: 0.000010
+[2026-01-11 21:44:07] Epoch: 439 | Iter: 000439 | Step Time: 13.702s | Local Loss: 0.0052 | Avg Loss: 0.0316 | LR: 0.000010
+[2026-01-11 21:44:26] Epoch: 440 | Iter: 000440 | Step Time: 12.863s | Local Loss: 0.1517 | Avg Loss: 0.0252 | LR: 0.000010
+[2026-01-11 21:44:45] Epoch: 441 | Iter: 000441 | Step Time: 13.773s | Local Loss: 0.0115 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-11 21:45:05] Epoch: 442 | Iter: 000442 | Step Time: 13.654s | Local Loss: 0.0119 | Avg Loss: 0.0499 | LR: 0.000010
+[2026-01-11 21:45:24] Epoch: 443 | Iter: 000443 | Step Time: 13.133s | Local Loss: 0.0094 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-11 21:45:43] Epoch: 444 | Iter: 000444 | Step Time: 13.826s | Local Loss: 0.0136 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-11 21:46:03] Epoch: 445 | Iter: 000445 | Step Time: 13.264s | Local Loss: 0.0065 | Avg Loss: 0.0909 | LR: 0.000010
+[2026-01-11 21:46:22] Epoch: 446 | Iter: 000446 | Step Time: 13.958s | Local Loss: 0.0253 | Avg Loss: 0.0131 | LR: 0.000010
+[2026-01-11 21:46:42] Epoch: 447 | Iter: 000447 | Step Time: 13.450s | Local Loss: 0.0094 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:47:01] Epoch: 448 | Iter: 000448 | Step Time: 13.598s | Local Loss: 0.0618 | Avg Loss: 0.0644 | LR: 0.000010
+[2026-01-11 21:47:21] Epoch: 449 | Iter: 000449 | Step Time: 14.463s | Local Loss: 0.0226 | Avg Loss: 0.0537 | LR: 0.000010
+[2026-01-11 21:50:26] Epoch: 450 | Iter: 000450 | Step Time: 21.128s | Local Loss: 0.2533 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 21:50:50] Epoch: 451 | Iter: 000451 | Step Time: 17.219s | Local Loss: 0.0178 | Avg Loss: 0.0139 | LR: 0.000010
+[2026-01-11 21:51:13] Epoch: 452 | Iter: 000452 | Step Time: 16.284s | Local Loss: 0.0422 | Avg Loss: 0.0249 | LR: 0.000010
+[2026-01-11 21:51:37] Epoch: 453 | Iter: 000453 | Step Time: 17.038s | Local Loss: 0.0113 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-11 21:52:01] Epoch: 454 | Iter: 000454 | Step Time: 16.908s | Local Loss: 0.0116 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-11 21:52:25] Epoch: 455 | Iter: 000455 | Step Time: 16.683s | Local Loss: 0.0186 | Avg Loss: 0.0119 | LR: 0.000010
+[2026-01-11 21:52:50] Epoch: 456 | Iter: 000456 | Step Time: 18.495s | Local Loss: 0.0809 | Avg Loss: 0.0581 | LR: 0.000010
+[2026-01-11 21:53:15] Epoch: 457 | Iter: 000457 | Step Time: 18.496s | Local Loss: 0.0100 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-11 21:53:40] Epoch: 458 | Iter: 000458 | Step Time: 18.001s | Local Loss: 0.0127 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-11 21:54:04] Epoch: 459 | Iter: 000459 | Step Time: 17.311s | Local Loss: 0.0975 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-11 21:54:29] Epoch: 460 | Iter: 000460 | Step Time: 17.680s | Local Loss: 0.0111 | Avg Loss: 0.0729 | LR: 0.000010
+[2026-01-11 21:54:54] Epoch: 461 | Iter: 000461 | Step Time: 18.348s | Local Loss: 0.0359 | Avg Loss: 0.0298 | LR: 0.000010
+[2026-01-11 21:55:14] Epoch: 462 | Iter: 000462 | Step Time: 14.465s | Local Loss: 0.0113 | Avg Loss: 0.0935 | LR: 0.000010
+[2026-01-11 21:55:33] Epoch: 463 | Iter: 000463 | Step Time: 13.685s | Local Loss: 0.0097 | Avg Loss: 0.0792 | LR: 0.000010
+[2026-01-11 21:55:53] Epoch: 464 | Iter: 000464 | Step Time: 13.827s | Local Loss: 0.0994 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-11 21:56:13] Epoch: 465 | Iter: 000465 | Step Time: 14.154s | Local Loss: 0.0096 | Avg Loss: 0.0175 | LR: 0.000010
+[2026-01-11 21:56:32] Epoch: 466 | Iter: 000466 | Step Time: 13.431s | Local Loss: 0.0951 | Avg Loss: 0.0673 | LR: 0.000010
+[2026-01-11 21:56:51] Epoch: 467 | Iter: 000467 | Step Time: 13.580s | Local Loss: 0.0140 | Avg Loss: 0.0220 | LR: 0.000010
+[2026-01-11 21:57:13] Epoch: 468 | Iter: 000468 | Step Time: 15.635s | Local Loss: 0.0000 | Avg Loss: 0.0102 | LR: 0.000010
+[2026-01-11 21:57:32] Epoch: 469 | Iter: 000469 | Step Time: 13.248s | Local Loss: 0.0226 | Avg Loss: 0.0268 | LR: 0.000010
+[2026-01-11 21:57:52] Epoch: 470 | Iter: 000470 | Step Time: 14.008s | Local Loss: 0.0070 | Avg Loss: 0.0140 | LR: 0.000010
+[2026-01-11 21:58:11] Epoch: 471 | Iter: 000471 | Step Time: 13.933s | Local Loss: 0.0151 | Avg Loss: 0.0492 | LR: 0.000010
+[2026-01-11 21:58:30] Epoch: 472 | Iter: 000472 | Step Time: 13.398s | Local Loss: 0.0153 | Avg Loss: 0.0741 | LR: 0.000010
+[2026-01-11 21:58:50] Epoch: 473 | Iter: 000473 | Step Time: 14.301s | Local Loss: 0.0124 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-11 21:59:09] Epoch: 474 | Iter: 000474 | Step Time: 13.114s | Local Loss: 0.0092 | Avg Loss: 0.0057 | LR: 0.000010
+[2026-01-11 21:59:29] Epoch: 475 | Iter: 000475 | Step Time: 13.736s | Local Loss: 0.0115 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 21:59:48] Epoch: 476 | Iter: 000476 | Step Time: 13.763s | Local Loss: 0.0320 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-11 22:00:09] Epoch: 477 | Iter: 000477 | Step Time: 14.601s | Local Loss: 0.0310 | Avg Loss: 0.0217 | LR: 0.000010
+[2026-01-11 22:00:28] Epoch: 478 | Iter: 000478 | Step Time: 13.076s | Local Loss: 0.0238 | Avg Loss: 0.1116 | LR: 0.000010
+[2026-01-11 22:00:48] Epoch: 479 | Iter: 000479 | Step Time: 14.254s | Local Loss: 0.0505 | Avg Loss: 0.0243 | LR: 0.000010
+[2026-01-11 22:01:08] Epoch: 480 | Iter: 000480 | Step Time: 14.806s | Local Loss: 0.0114 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:01:28] Epoch: 481 | Iter: 000481 | Step Time: 14.087s | Local Loss: 0.0842 | Avg Loss: 0.0197 | LR: 0.000010
+[2026-01-11 22:01:48] Epoch: 482 | Iter: 000482 | Step Time: 14.341s | Local Loss: 0.0150 | Avg Loss: 0.0244 | LR: 0.000010
+[2026-01-11 22:02:08] Epoch: 483 | Iter: 000483 | Step Time: 14.342s | Local Loss: 0.0078 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-11 22:02:29] Epoch: 484 | Iter: 000484 | Step Time: 14.461s | Local Loss: 0.0680 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-11 22:02:48] Epoch: 485 | Iter: 000485 | Step Time: 13.589s | Local Loss: 0.0162 | Avg Loss: 0.0634 | LR: 0.000010
+[2026-01-11 22:03:08] Epoch: 486 | Iter: 000486 | Step Time: 14.660s | Local Loss: 0.0584 | Avg Loss: 0.0231 | LR: 0.000010
+[2026-01-11 22:03:29] Epoch: 487 | Iter: 000487 | Step Time: 14.490s | Local Loss: 0.0268 | Avg Loss: 0.0840 | LR: 0.000010
+[2026-01-11 22:03:48] Epoch: 488 | Iter: 000488 | Step Time: 13.504s | Local Loss: 0.0322 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-11 22:04:08] Epoch: 489 | Iter: 000489 | Step Time: 14.340s | Local Loss: 0.0214 | Avg Loss: 0.0474 | LR: 0.000010
+[2026-01-11 22:04:28] Epoch: 490 | Iter: 000490 | Step Time: 14.160s | Local Loss: 0.0162 | Avg Loss: 0.0407 | LR: 0.000010
+[2026-01-11 22:04:47] Epoch: 491 | Iter: 000491 | Step Time: 13.447s | Local Loss: 0.0117 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-11 22:05:06] Epoch: 492 | Iter: 000492 | Step Time: 13.156s | Local Loss: 0.0087 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-11 22:05:28] Epoch: 493 | Iter: 000493 | Step Time: 15.567s | Local Loss: 0.0399 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-11 22:05:47] Epoch: 494 | Iter: 000494 | Step Time: 13.154s | Local Loss: 0.0661 | Avg Loss: 0.0114 | LR: 0.000010
+[2026-01-11 22:06:06] Epoch: 495 | Iter: 000495 | Step Time: 13.700s | Local Loss: 0.0134 | Avg Loss: 0.0250 | LR: 0.000010
+[2026-01-11 22:06:26] Epoch: 496 | Iter: 000496 | Step Time: 13.731s | Local Loss: 0.0097 | Avg Loss: 0.0155 | LR: 0.000010
+[2026-01-11 22:06:45] Epoch: 497 | Iter: 000497 | Step Time: 13.538s | Local Loss: 0.0291 | Avg Loss: 0.0393 | LR: 0.000010
+[2026-01-11 22:07:05] Epoch: 498 | Iter: 000498 | Step Time: 14.122s | Local Loss: 0.0915 | Avg Loss: 0.0247 | LR: 0.000010
+[2026-01-11 22:07:24] Epoch: 499 | Iter: 000499 | Step Time: 13.375s | Local Loss: 0.0136 | Avg Loss: 0.0265 | LR: 0.000010
+[2026-01-11 22:41:07] Logger closing.
+[2026-01-12 05:01:27] Logger initialized. Current global_step: 0
+[2026-01-12 05:02:30] Epoch: 0 | Iter: 000000 | Step Time: 14.494s | Local Loss: 0.1366 | Avg Loss: 0.3175 | LR: 0.000010
+[2026-01-12 05:02:49] Epoch: 1 | Iter: 000001 | Step Time: 13.736s | Local Loss: 0.2651 | Avg Loss: 0.3606 | LR: 0.000010
+[2026-01-12 05:03:08] Epoch: 2 | Iter: 000002 | Step Time: 14.186s | Local Loss: 0.0395 | Avg Loss: 0.2905 | LR: 0.000010
+[2026-01-12 05:03:25] Epoch: 3 | Iter: 000003 | Step Time: 12.116s | Local Loss: 0.0851 | Avg Loss: 0.3149 | LR: 0.000010
+[2026-01-12 05:03:43] Epoch: 4 | Iter: 000004 | Step Time: 12.781s | Local Loss: 0.0737 | Avg Loss: 0.1308 | LR: 0.000010
+[2026-01-12 05:04:00] Epoch: 5 | Iter: 000005 | Step Time: 11.941s | Local Loss: 0.3565 | Avg Loss: 0.2456 | LR: 0.000010
+[2026-01-12 05:04:17] Epoch: 6 | Iter: 000006 | Step Time: 12.227s | Local Loss: 0.1354 | Avg Loss: 0.1624 | LR: 0.000010
+[2026-01-12 05:04:35] Epoch: 7 | Iter: 000007 | Step Time: 12.480s | Local Loss: 0.1025 | Avg Loss: 0.2101 | LR: 0.000010
+[2026-01-12 05:05:01] Epoch: 8 | Iter: 000008 | Step Time: 20.695s | Local Loss: 0.3537 | Avg Loss: 0.1680 | LR: 0.000010
+[2026-01-12 05:05:21] Epoch: 9 | Iter: 000009 | Step Time: 15.178s | Local Loss: 0.3681 | Avg Loss: 0.2822 | LR: 0.000010
+[2026-01-12 05:05:40] Epoch: 10 | Iter: 000010 | Step Time: 13.905s | Local Loss: 0.0936 | Avg Loss: 0.1154 | LR: 0.000010
+[2026-01-12 05:05:57] Epoch: 11 | Iter: 000011 | Step Time: 12.139s | Local Loss: 0.2471 | Avg Loss: 0.1757 | LR: 0.000010
+[2026-01-12 05:06:16] Epoch: 12 | Iter: 000012 | Step Time: 13.530s | Local Loss: 0.1537 | Avg Loss: 0.2487 | LR: 0.000010
+[2026-01-12 05:06:33] Epoch: 13 | Iter: 000013 | Step Time: 12.088s | Local Loss: 0.0267 | Avg Loss: 0.1926 | LR: 0.000010
+[2026-01-12 05:06:51] Epoch: 14 | Iter: 000014 | Step Time: 12.831s | Local Loss: 0.4775 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 05:07:08] Epoch: 15 | Iter: 000015 | Step Time: 12.236s | Local Loss: 0.1223 | Avg Loss: 0.2417 | LR: 0.000010
+[2026-01-12 05:07:25] Epoch: 16 | Iter: 000016 | Step Time: 11.974s | Local Loss: 0.0321 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 05:07:42] Epoch: 17 | Iter: 000017 | Step Time: 12.016s | Local Loss: 0.3635 | Avg Loss: 0.1943 | LR: 0.000010
+[2026-01-12 05:07:59] Epoch: 18 | Iter: 000018 | Step Time: 11.901s | Local Loss: 0.0205 | Avg Loss: 0.2336 | LR: 0.000010
+[2026-01-12 05:08:16] Epoch: 19 | Iter: 000019 | Step Time: 11.901s | Local Loss: 0.1696 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 05:08:34] Epoch: 20 | Iter: 000020 | Step Time: 12.859s | Local Loss: 0.1780 | Avg Loss: 0.1515 | LR: 0.000010
+[2026-01-12 05:08:50] Epoch: 21 | Iter: 000021 | Step Time: 11.713s | Local Loss: 0.1749 | Avg Loss: 0.0828 | LR: 0.000010
+[2026-01-12 05:09:08] Epoch: 22 | Iter: 000022 | Step Time: 12.920s | Local Loss: 0.2435 | Avg Loss: 0.1341 | LR: 0.000010
+[2026-01-12 05:09:25] Epoch: 23 | Iter: 000023 | Step Time: 11.901s | Local Loss: 0.0777 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 05:09:42] Epoch: 24 | Iter: 000024 | Step Time: 12.176s | Local Loss: 0.1247 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:10:00] Epoch: 25 | Iter: 000025 | Step Time: 12.423s | Local Loss: 0.3008 | Avg Loss: 0.1183 | LR: 0.000010
+[2026-01-12 05:10:17] Epoch: 26 | Iter: 000026 | Step Time: 11.711s | Local Loss: 0.2884 | Avg Loss: 0.2049 | LR: 0.000010
+[2026-01-12 05:10:33] Epoch: 27 | Iter: 000027 | Step Time: 11.823s | Local Loss: 0.4710 | Avg Loss: 0.1025 | LR: 0.000010
+[2026-01-12 05:10:51] Epoch: 28 | Iter: 000028 | Step Time: 12.252s | Local Loss: 0.2095 | Avg Loss: 0.1752 | LR: 0.000010
+[2026-01-12 05:11:08] Epoch: 29 | Iter: 000029 | Step Time: 12.281s | Local Loss: 0.0829 | Avg Loss: 0.3770 | LR: 0.000010
+[2026-01-12 05:11:25] Epoch: 30 | Iter: 000030 | Step Time: 11.597s | Local Loss: 0.5373 | Avg Loss: 0.1039 | LR: 0.000010
+[2026-01-12 05:11:43] Epoch: 31 | Iter: 000031 | Step Time: 13.160s | Local Loss: 0.1342 | Avg Loss: 0.0606 | LR: 0.000010
+[2026-01-12 05:11:59] Epoch: 32 | Iter: 000032 | Step Time: 11.465s | Local Loss: 0.0735 | Avg Loss: 0.1954 | LR: 0.000010
+[2026-01-12 05:12:16] Epoch: 33 | Iter: 000033 | Step Time: 11.677s | Local Loss: 0.3482 | Avg Loss: 0.1147 | LR: 0.000010
+[2026-01-12 05:12:33] Epoch: 34 | Iter: 000034 | Step Time: 12.191s | Local Loss: 0.0501 | Avg Loss: 0.2043 | LR: 0.000010
+[2026-01-12 05:12:50] Epoch: 35 | Iter: 000035 | Step Time: 12.285s | Local Loss: 0.0438 | Avg Loss: 0.1276 | LR: 0.000010
+[2026-01-12 05:13:07] Epoch: 36 | Iter: 000036 | Step Time: 11.755s | Local Loss: 0.4746 | Avg Loss: 0.1865 | LR: 0.000010
+[2026-01-12 05:13:24] Epoch: 37 | Iter: 000037 | Step Time: 12.190s | Local Loss: 0.0874 | Avg Loss: 0.2437 | LR: 0.000010
+[2026-01-12 05:13:41] Epoch: 38 | Iter: 000038 | Step Time: 11.833s | Local Loss: 0.5426 | Avg Loss: 0.2064 | LR: 0.000010
+[2026-01-12 05:13:58] Epoch: 39 | Iter: 000039 | Step Time: 11.806s | Local Loss: 0.1497 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 05:14:15] Epoch: 40 | Iter: 000040 | Step Time: 11.930s | Local Loss: 0.0501 | Avg Loss: 0.1293 | LR: 0.000010
+[2026-01-12 05:14:31] Epoch: 41 | Iter: 000041 | Step Time: 11.445s | Local Loss: 0.5416 | Avg Loss: 0.2445 | LR: 0.000010
+[2026-01-12 05:14:48] Epoch: 42 | Iter: 000042 | Step Time: 11.976s | Local Loss: 0.1406 | Avg Loss: 0.2406 | LR: 0.000010
+[2026-01-12 05:15:06] Epoch: 43 | Iter: 000043 | Step Time: 12.952s | Local Loss: 0.1091 | Avg Loss: 0.0817 | LR: 0.000010
+[2026-01-12 05:15:23] Epoch: 44 | Iter: 000044 | Step Time: 11.677s | Local Loss: 0.0518 | Avg Loss: 0.2296 | LR: 0.000010
+[2026-01-12 05:15:40] Epoch: 45 | Iter: 000045 | Step Time: 11.935s | Local Loss: 0.2682 | Avg Loss: 0.1002 | LR: 0.000010
+[2026-01-12 05:15:56] Epoch: 46 | Iter: 000046 | Step Time: 11.812s | Local Loss: 0.3772 | Avg Loss: 0.0756 | LR: 0.000010
+[2026-01-12 05:16:13] Epoch: 47 | Iter: 000047 | Step Time: 11.787s | Local Loss: 0.3175 | Avg Loss: 0.2498 | LR: 0.000010
+[2026-01-12 05:16:30] Epoch: 48 | Iter: 000048 | Step Time: 11.804s | Local Loss: 0.3498 | Avg Loss: 0.1358 | LR: 0.000010
+[2026-01-12 05:16:47] Epoch: 49 | Iter: 000049 | Step Time: 11.867s | Local Loss: 0.1351 | Avg Loss: 0.1306 | LR: 0.000010
+[2026-01-12 05:19:56] Epoch: 50 | Iter: 000050 | Step Time: 20.853s | Local Loss: 0.0504 | Avg Loss: 0.0549 | LR: 0.000010
+[2026-01-12 05:20:18] Epoch: 51 | Iter: 000051 | Step Time: 15.440s | Local Loss: 0.5208 | Avg Loss: 0.1040 | LR: 0.000010
+[2026-01-12 05:20:39] Epoch: 52 | Iter: 000052 | Step Time: 15.592s | Local Loss: 0.1596 | Avg Loss: 0.1927 | LR: 0.000010
+[2026-01-12 05:20:59] Epoch: 53 | Iter: 000053 | Step Time: 13.759s | Local Loss: 0.4288 | Avg Loss: 0.1603 | LR: 0.000010
+[2026-01-12 05:21:18] Epoch: 54 | Iter: 000054 | Step Time: 13.306s | Local Loss: 0.1687 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 05:21:37] Epoch: 55 | Iter: 000055 | Step Time: 13.819s | Local Loss: 0.0325 | Avg Loss: 0.0942 | LR: 0.000010
+[2026-01-12 05:21:57] Epoch: 56 | Iter: 000056 | Step Time: 14.655s | Local Loss: 0.4038 | Avg Loss: 0.1573 | LR: 0.000010
+[2026-01-12 05:22:16] Epoch: 57 | Iter: 000057 | Step Time: 13.199s | Local Loss: 0.0347 | Avg Loss: 0.0819 | LR: 0.000010
+[2026-01-12 05:22:35] Epoch: 58 | Iter: 000058 | Step Time: 12.785s | Local Loss: 0.0234 | Avg Loss: 0.2202 | LR: 0.000010
+[2026-01-12 05:22:54] Epoch: 59 | Iter: 000059 | Step Time: 13.342s | Local Loss: 0.0987 | Avg Loss: 0.2177 | LR: 0.000010
+[2026-01-12 05:23:13] Epoch: 60 | Iter: 000060 | Step Time: 13.286s | Local Loss: 0.1756 | Avg Loss: 0.2884 | LR: 0.000010
+[2026-01-12 05:23:31] Epoch: 61 | Iter: 000061 | Step Time: 12.907s | Local Loss: 0.0494 | Avg Loss: 0.1094 | LR: 0.000010
+[2026-01-12 05:23:50] Epoch: 62 | Iter: 000062 | Step Time: 13.403s | Local Loss: 0.5617 | Avg Loss: 0.1256 | LR: 0.000010
+[2026-01-12 05:24:08] Epoch: 63 | Iter: 000063 | Step Time: 12.939s | Local Loss: 0.0642 | Avg Loss: 0.3026 | LR: 0.000010
+[2026-01-12 05:24:27] Epoch: 64 | Iter: 000064 | Step Time: 12.829s | Local Loss: 0.2456 | Avg Loss: 0.2045 | LR: 0.000010
+[2026-01-12 05:24:46] Epoch: 65 | Iter: 000065 | Step Time: 13.175s | Local Loss: 0.0751 | Avg Loss: 0.1510 | LR: 0.000010
+[2026-01-12 05:25:04] Epoch: 66 | Iter: 000066 | Step Time: 12.838s | Local Loss: 0.1801 | Avg Loss: 0.2916 | LR: 0.000010
+[2026-01-12 05:25:23] Epoch: 67 | Iter: 000067 | Step Time: 13.256s | Local Loss: 0.0215 | Avg Loss: 0.0622 | LR: 0.000010
+[2026-01-12 05:25:41] Epoch: 68 | Iter: 000068 | Step Time: 12.631s | Local Loss: 0.2164 | Avg Loss: 0.1640 | LR: 0.000010
+[2026-01-12 05:26:00] Epoch: 69 | Iter: 000069 | Step Time: 12.945s | Local Loss: 0.2977 | Avg Loss: 0.3640 | LR: 0.000010
+[2026-01-12 05:26:18] Epoch: 70 | Iter: 000070 | Step Time: 12.893s | Local Loss: 0.0406 | Avg Loss: 0.0993 | LR: 0.000010
+[2026-01-12 05:26:37] Epoch: 71 | Iter: 000071 | Step Time: 13.075s | Local Loss: 0.2265 | Avg Loss: 0.0624 | LR: 0.000010
+[2026-01-12 05:26:55] Epoch: 72 | Iter: 000072 | Step Time: 13.220s | Local Loss: 0.2381 | Avg Loss: 0.0705 | LR: 0.000010
+[2026-01-12 05:27:14] Epoch: 73 | Iter: 000073 | Step Time: 13.088s | Local Loss: 0.0396 | Avg Loss: 0.2174 | LR: 0.000010
+[2026-01-12 05:27:33] Epoch: 74 | Iter: 000074 | Step Time: 13.080s | Local Loss: 0.0448 | Avg Loss: 0.1231 | LR: 0.000010
+[2026-01-12 05:27:52] Epoch: 75 | Iter: 000075 | Step Time: 13.342s | Local Loss: 0.0990 | Avg Loss: 0.1885 | LR: 0.000010
+[2026-01-12 05:28:10] Epoch: 76 | Iter: 000076 | Step Time: 12.827s | Local Loss: 0.0505 | Avg Loss: 0.0648 | LR: 0.000010
+[2026-01-12 05:28:29] Epoch: 77 | Iter: 000077 | Step Time: 13.146s | Local Loss: 0.2127 | Avg Loss: 0.0611 | LR: 0.000010
+[2026-01-12 05:28:48] Epoch: 78 | Iter: 000078 | Step Time: 13.409s | Local Loss: 0.4587 | Avg Loss: 0.1766 | LR: 0.000010
+[2026-01-12 05:29:06] Epoch: 79 | Iter: 000079 | Step Time: 13.027s | Local Loss: 0.3936 | Avg Loss: 0.1301 | LR: 0.000010
+[2026-01-12 05:29:25] Epoch: 80 | Iter: 000080 | Step Time: 12.848s | Local Loss: 0.2887 | Avg Loss: 0.0990 | LR: 0.000010
+[2026-01-12 05:29:43] Epoch: 81 | Iter: 000081 | Step Time: 12.954s | Local Loss: 0.0954 | Avg Loss: 0.1324 | LR: 0.000010
+[2026-01-12 05:30:01] Epoch: 82 | Iter: 000082 | Step Time: 12.669s | Local Loss: 0.0318 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 05:30:20] Epoch: 83 | Iter: 000083 | Step Time: 13.305s | Local Loss: 0.0320 | Avg Loss: 0.1965 | LR: 0.000010
+[2026-01-12 05:30:38] Epoch: 84 | Iter: 000084 | Step Time: 12.889s | Local Loss: 0.2765 | Avg Loss: 0.1695 | LR: 0.000010
+[2026-01-12 05:30:57] Epoch: 85 | Iter: 000085 | Step Time: 12.810s | Local Loss: 0.0792 | Avg Loss: 0.0692 | LR: 0.000010
+[2026-01-12 05:31:15] Epoch: 86 | Iter: 000086 | Step Time: 13.180s | Local Loss: 0.0752 | Avg Loss: 0.2215 | LR: 0.000010
+[2026-01-12 05:31:34] Epoch: 87 | Iter: 000087 | Step Time: 12.792s | Local Loss: 0.0609 | Avg Loss: 0.1069 | LR: 0.000010
+[2026-01-12 05:31:52] Epoch: 88 | Iter: 000088 | Step Time: 12.953s | Local Loss: 0.3953 | Avg Loss: 0.1401 | LR: 0.000010
+[2026-01-12 05:32:11] Epoch: 89 | Iter: 000089 | Step Time: 12.843s | Local Loss: 0.0361 | Avg Loss: 0.0704 | LR: 0.000010
+[2026-01-12 05:32:29] Epoch: 90 | Iter: 000090 | Step Time: 12.594s | Local Loss: 0.0637 | Avg Loss: 0.1796 | LR: 0.000010
+[2026-01-12 05:32:48] Epoch: 91 | Iter: 000091 | Step Time: 13.332s | Local Loss: 0.1628 | Avg Loss: 0.1057 | LR: 0.000010
+[2026-01-12 05:33:06] Epoch: 92 | Iter: 000092 | Step Time: 12.835s | Local Loss: 0.0411 | Avg Loss: 0.1803 | LR: 0.000010
+[2026-01-12 05:33:25] Epoch: 93 | Iter: 000093 | Step Time: 13.068s | Local Loss: 0.0203 | Avg Loss: 0.0595 | LR: 0.000010
+[2026-01-12 05:33:43] Epoch: 94 | Iter: 000094 | Step Time: 13.169s | Local Loss: 0.0734 | Avg Loss: 0.1193 | LR: 0.000010
+[2026-01-12 05:34:02] Epoch: 95 | Iter: 000095 | Step Time: 12.975s | Local Loss: 0.3920 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 05:34:21] Epoch: 96 | Iter: 000096 | Step Time: 13.209s | Local Loss: 0.0518 | Avg Loss: 0.1657 | LR: 0.000010
+[2026-01-12 05:34:39] Epoch: 97 | Iter: 000097 | Step Time: 12.819s | Local Loss: 0.4613 | Avg Loss: 0.1220 | LR: 0.000010
+[2026-01-12 05:34:57] Epoch: 98 | Iter: 000098 | Step Time: 12.874s | Local Loss: 0.0061 | Avg Loss: 0.0437 | LR: 0.000010
+[2026-01-12 05:35:16] Epoch: 99 | Iter: 000099 | Step Time: 13.207s | Local Loss: 0.0874 | Avg Loss: 0.0858 | LR: 0.000010
+[2026-01-12 05:38:23] Epoch: 100 | Iter: 000100 | Step Time: 19.948s | Local Loss: 0.0588 | Avg Loss: 0.1737 | LR: 0.000010
+[2026-01-12 05:38:47] Epoch: 101 | Iter: 000101 | Step Time: 16.981s | Local Loss: 0.3687 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:39:12] Epoch: 102 | Iter: 000102 | Step Time: 18.013s | Local Loss: 0.0464 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 05:39:35] Epoch: 103 | Iter: 000103 | Step Time: 16.322s | Local Loss: 0.3424 | Avg Loss: 0.0923 | LR: 0.000010
+[2026-01-12 05:39:59] Epoch: 104 | Iter: 000104 | Step Time: 16.993s | Local Loss: 0.0642 | Avg Loss: 0.2451 | LR: 0.000010
+[2026-01-12 05:40:23] Epoch: 105 | Iter: 000105 | Step Time: 17.497s | Local Loss: 0.1176 | Avg Loss: 0.2262 | LR: 0.000010
+[2026-01-12 05:40:47] Epoch: 106 | Iter: 000106 | Step Time: 17.072s | Local Loss: 0.2704 | Avg Loss: 0.2830 | LR: 0.000010
+[2026-01-12 05:41:12] Epoch: 107 | Iter: 000107 | Step Time: 18.425s | Local Loss: 0.0485 | Avg Loss: 0.2578 | LR: 0.000010
+[2026-01-12 05:41:31] Epoch: 108 | Iter: 000108 | Step Time: 13.508s | Local Loss: 0.5036 | Avg Loss: 0.1349 | LR: 0.000010
+[2026-01-12 05:41:51] Epoch: 109 | Iter: 000109 | Step Time: 14.263s | Local Loss: 0.0897 | Avg Loss: 0.1729 | LR: 0.000010
+[2026-01-12 05:42:10] Epoch: 110 | Iter: 000110 | Step Time: 13.360s | Local Loss: 0.2178 | Avg Loss: 0.1103 | LR: 0.000010
+[2026-01-12 05:42:30] Epoch: 111 | Iter: 000111 | Step Time: 13.631s | Local Loss: 0.0676 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:42:49] Epoch: 112 | Iter: 000112 | Step Time: 13.531s | Local Loss: 0.0203 | Avg Loss: 0.1677 | LR: 0.000010
+[2026-01-12 05:43:08] Epoch: 113 | Iter: 000113 | Step Time: 13.254s | Local Loss: 0.0402 | Avg Loss: 0.0890 | LR: 0.000010
+[2026-01-12 05:43:27] Epoch: 114 | Iter: 000114 | Step Time: 13.536s | Local Loss: 0.2429 | Avg Loss: 0.1955 | LR: 0.000010
+[2026-01-12 05:43:46] Epoch: 115 | Iter: 000115 | Step Time: 13.381s | Local Loss: 0.3709 | Avg Loss: 0.1096 | LR: 0.000010
+[2026-01-12 05:44:05] Epoch: 116 | Iter: 000116 | Step Time: 13.332s | Local Loss: 0.0815 | Avg Loss: 0.2664 | LR: 0.000010
+[2026-01-12 05:44:24] Epoch: 117 | Iter: 000117 | Step Time: 13.678s | Local Loss: 0.2852 | Avg Loss: 0.1568 | LR: 0.000010
+[2026-01-12 05:44:44] Epoch: 118 | Iter: 000118 | Step Time: 14.191s | Local Loss: 0.0194 | Avg Loss: 0.2051 | LR: 0.000010
+[2026-01-12 05:45:03] Epoch: 119 | Iter: 000119 | Step Time: 13.413s | Local Loss: 0.2439 | Avg Loss: 0.0427 | LR: 0.000010
+[2026-01-12 05:45:23] Epoch: 120 | Iter: 000120 | Step Time: 13.526s | Local Loss: 0.0794 | Avg Loss: 0.1476 | LR: 0.000010
+[2026-01-12 05:45:42] Epoch: 121 | Iter: 000121 | Step Time: 13.488s | Local Loss: 0.0501 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:46:01] Epoch: 122 | Iter: 000122 | Step Time: 13.177s | Local Loss: 0.0934 | Avg Loss: 0.1647 | LR: 0.000010
+[2026-01-12 05:46:20] Epoch: 123 | Iter: 000123 | Step Time: 13.239s | Local Loss: 0.0513 | Avg Loss: 0.2644 | LR: 0.000010
+[2026-01-12 05:46:39] Epoch: 124 | Iter: 000124 | Step Time: 13.156s | Local Loss: 0.1005 | Avg Loss: 0.2084 | LR: 0.000010
+[2026-01-12 05:46:59] Epoch: 125 | Iter: 000125 | Step Time: 14.170s | Local Loss: 0.1318 | Avg Loss: 0.0356 | LR: 0.000010
+[2026-01-12 05:47:18] Epoch: 126 | Iter: 000126 | Step Time: 13.523s | Local Loss: 0.1196 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 05:47:37] Epoch: 127 | Iter: 000127 | Step Time: 13.264s | Local Loss: 0.4100 | Avg Loss: 0.1122 | LR: 0.000010
+[2026-01-12 05:47:56] Epoch: 128 | Iter: 000128 | Step Time: 13.837s | Local Loss: 0.0645 | Avg Loss: 0.1085 | LR: 0.000010
+[2026-01-12 05:48:16] Epoch: 129 | Iter: 000129 | Step Time: 13.677s | Local Loss: 0.0313 | Avg Loss: 0.1843 | LR: 0.000010
+[2026-01-12 05:48:35] Epoch: 130 | Iter: 000130 | Step Time: 13.358s | Local Loss: 0.3409 | Avg Loss: 0.0338 | LR: 0.000010
+[2026-01-12 05:48:54] Epoch: 131 | Iter: 000131 | Step Time: 13.611s | Local Loss: 0.0651 | Avg Loss: 0.1511 | LR: 0.000010
+[2026-01-12 05:49:13] Epoch: 132 | Iter: 000132 | Step Time: 13.482s | Local Loss: 0.2910 | Avg Loss: 0.3154 | LR: 0.000010
+[2026-01-12 05:49:33] Epoch: 133 | Iter: 000133 | Step Time: 13.856s | Local Loss: 0.2399 | Avg Loss: 0.0927 | LR: 0.000010
+[2026-01-12 05:49:52] Epoch: 134 | Iter: 000134 | Step Time: 13.513s | Local Loss: 0.0869 | Avg Loss: 0.1516 | LR: 0.000010
+[2026-01-12 05:50:11] Epoch: 135 | Iter: 000135 | Step Time: 13.021s | Local Loss: 0.3080 | Avg Loss: 0.1054 | LR: 0.000010
+[2026-01-12 05:50:30] Epoch: 136 | Iter: 000136 | Step Time: 13.799s | Local Loss: 0.2081 | Avg Loss: 0.1385 | LR: 0.000010
+[2026-01-12 05:50:50] Epoch: 137 | Iter: 000137 | Step Time: 13.477s | Local Loss: 0.1915 | Avg Loss: 0.2147 | LR: 0.000010
+[2026-01-12 05:51:09] Epoch: 138 | Iter: 000138 | Step Time: 13.461s | Local Loss: 0.0935 | Avg Loss: 0.0361 | LR: 0.000010
+[2026-01-12 05:51:28] Epoch: 139 | Iter: 000139 | Step Time: 13.650s | Local Loss: 0.0970 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 05:51:48] Epoch: 140 | Iter: 000140 | Step Time: 13.912s | Local Loss: 0.5067 | Avg Loss: 0.0681 | LR: 0.000010
+[2026-01-12 05:52:07] Epoch: 141 | Iter: 000141 | Step Time: 13.957s | Local Loss: 0.0438 | Avg Loss: 0.1011 | LR: 0.000010
+[2026-01-12 05:52:27] Epoch: 142 | Iter: 000142 | Step Time: 13.705s | Local Loss: 0.0100 | Avg Loss: 0.0585 | LR: 0.000010
+[2026-01-12 05:52:46] Epoch: 143 | Iter: 000143 | Step Time: 13.139s | Local Loss: 0.2014 | Avg Loss: 0.1478 | LR: 0.000010
+[2026-01-12 05:53:05] Epoch: 144 | Iter: 000144 | Step Time: 13.816s | Local Loss: 0.1901 | Avg Loss: 0.1655 | LR: 0.000010
+[2026-01-12 05:53:24] Epoch: 145 | Iter: 000145 | Step Time: 13.453s | Local Loss: 0.1113 | Avg Loss: 0.1662 | LR: 0.000010
+[2026-01-12 05:53:44] Epoch: 146 | Iter: 000146 | Step Time: 13.445s | Local Loss: 0.0750 | Avg Loss: 0.1164 | LR: 0.000010
+[2026-01-12 05:54:03] Epoch: 147 | Iter: 000147 | Step Time: 13.244s | Local Loss: 0.0000 | Avg Loss: 0.1229 | LR: 0.000010
+[2026-01-12 05:54:22] Epoch: 148 | Iter: 000148 | Step Time: 13.442s | Local Loss: 0.0408 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 05:54:41] Epoch: 149 | Iter: 000149 | Step Time: 13.783s | Local Loss: 0.3773 | Avg Loss: 0.0693 | LR: 0.000010
+[2026-01-12 05:57:45] Epoch: 150 | Iter: 000150 | Step Time: 19.656s | Local Loss: 0.1312 | Avg Loss: 0.1789 | LR: 0.000010
+[2026-01-12 05:58:09] Epoch: 151 | Iter: 000151 | Step Time: 16.923s | Local Loss: 0.0341 | Avg Loss: 0.1895 | LR: 0.000010
+[2026-01-12 05:58:33] Epoch: 152 | Iter: 000152 | Step Time: 17.803s | Local Loss: 0.0209 | Avg Loss: 0.0646 | LR: 0.000010
+[2026-01-12 05:58:57] Epoch: 153 | Iter: 000153 | Step Time: 16.635s | Local Loss: 0.0833 | Avg Loss: 0.0776 | LR: 0.000010
+[2026-01-12 05:59:21] Epoch: 154 | Iter: 000154 | Step Time: 17.764s | Local Loss: 0.0524 | Avg Loss: 0.0744 | LR: 0.000010
+[2026-01-12 05:59:44] Epoch: 155 | Iter: 000155 | Step Time: 16.389s | Local Loss: 0.4825 | Avg Loss: 0.1184 | LR: 0.000010
+[2026-01-12 06:00:08] Epoch: 156 | Iter: 000156 | Step Time: 17.138s | Local Loss: 0.1719 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 06:00:32] Epoch: 157 | Iter: 000157 | Step Time: 17.369s | Local Loss: 0.3164 | Avg Loss: 0.2156 | LR: 0.000010
+[2026-01-12 06:00:57] Epoch: 158 | Iter: 000158 | Step Time: 17.828s | Local Loss: 0.0236 | Avg Loss: 0.0973 | LR: 0.000010
+[2026-01-12 06:01:21] Epoch: 159 | Iter: 000159 | Step Time: 16.705s | Local Loss: 0.1704 | Avg Loss: 0.1312 | LR: 0.000010
+[2026-01-12 06:01:47] Epoch: 160 | Iter: 000160 | Step Time: 19.127s | Local Loss: 0.0419 | Avg Loss: 0.1779 | LR: 0.000010
+[2026-01-12 06:02:06] Epoch: 161 | Iter: 000161 | Step Time: 13.154s | Local Loss: 0.0944 | Avg Loss: 0.2179 | LR: 0.000010
+[2026-01-12 06:02:26] Epoch: 162 | Iter: 000162 | Step Time: 14.096s | Local Loss: 0.1127 | Avg Loss: 0.1022 | LR: 0.000010
+[2026-01-12 06:02:45] Epoch: 163 | Iter: 000163 | Step Time: 13.563s | Local Loss: 0.0578 | Avg Loss: 0.1186 | LR: 0.000010
+[2026-01-12 06:03:04] Epoch: 164 | Iter: 000164 | Step Time: 13.536s | Local Loss: 0.0749 | Avg Loss: 0.0916 | LR: 0.000010
+[2026-01-12 06:03:23] Epoch: 165 | Iter: 000165 | Step Time: 13.621s | Local Loss: 0.0489 | Avg Loss: 0.1610 | LR: 0.000010
+[2026-01-12 06:03:42] Epoch: 166 | Iter: 000166 | Step Time: 12.985s | Local Loss: 0.0100 | Avg Loss: 0.0938 | LR: 0.000010
+[2026-01-12 06:04:02] Epoch: 167 | Iter: 000167 | Step Time: 13.647s | Local Loss: 0.0849 | Avg Loss: 0.0735 | LR: 0.000010
+[2026-01-12 06:04:21] Epoch: 168 | Iter: 000168 | Step Time: 13.292s | Local Loss: 0.0215 | Avg Loss: 0.0855 | LR: 0.000010
+[2026-01-12 06:04:39] Epoch: 169 | Iter: 000169 | Step Time: 13.151s | Local Loss: 0.0238 | Avg Loss: 0.1170 | LR: 0.000010
+[2026-01-12 06:04:59] Epoch: 170 | Iter: 000170 | Step Time: 13.492s | Local Loss: 0.1581 | Avg Loss: 0.0894 | LR: 0.000010
+[2026-01-12 06:05:18] Epoch: 171 | Iter: 000171 | Step Time: 12.977s | Local Loss: 0.1066 | Avg Loss: 0.0932 | LR: 0.000010
+[2026-01-12 06:05:37] Epoch: 172 | Iter: 000172 | Step Time: 13.179s | Local Loss: 0.0444 | Avg Loss: 0.2081 | LR: 0.000010
+[2026-01-12 06:05:55] Epoch: 173 | Iter: 000173 | Step Time: 12.982s | Local Loss: 0.0360 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 06:06:15] Epoch: 174 | Iter: 000174 | Step Time: 13.149s | Local Loss: 0.0978 | Avg Loss: 0.1458 | LR: 0.000010
+[2026-01-12 06:06:34] Epoch: 175 | Iter: 000175 | Step Time: 13.560s | Local Loss: 0.1045 | Avg Loss: 0.1061 | LR: 0.000010
+[2026-01-12 06:06:53] Epoch: 176 | Iter: 000176 | Step Time: 13.268s | Local Loss: 0.0223 | Avg Loss: 0.1364 | LR: 0.000010
+[2026-01-12 06:07:12] Epoch: 177 | Iter: 000177 | Step Time: 12.940s | Local Loss: 0.0193 | Avg Loss: 0.0709 | LR: 0.000010
+[2026-01-12 06:07:31] Epoch: 178 | Iter: 000178 | Step Time: 13.825s | Local Loss: 0.1436 | Avg Loss: 0.1609 | LR: 0.000010
+[2026-01-12 06:07:51] Epoch: 179 | Iter: 000179 | Step Time: 13.609s | Local Loss: 0.0420 | Avg Loss: 0.1923 | LR: 0.000010
+[2026-01-12 06:08:10] Epoch: 180 | Iter: 000180 | Step Time: 13.368s | Local Loss: 0.0306 | Avg Loss: 0.0364 | LR: 0.000010
+[2026-01-12 06:08:29] Epoch: 181 | Iter: 000181 | Step Time: 13.602s | Local Loss: 0.0464 | Avg Loss: 0.0721 | LR: 0.000010
+[2026-01-12 06:08:49] Epoch: 182 | Iter: 000182 | Step Time: 13.788s | Local Loss: 0.0669 | Avg Loss: 0.1288 | LR: 0.000010
+[2026-01-12 06:09:08] Epoch: 183 | Iter: 000183 | Step Time: 13.676s | Local Loss: 0.0319 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:09:28] Epoch: 184 | Iter: 000184 | Step Time: 13.688s | Local Loss: 0.2420 | Avg Loss: 0.2206 | LR: 0.000010
+[2026-01-12 06:09:47] Epoch: 185 | Iter: 000185 | Step Time: 13.278s | Local Loss: 0.0242 | Avg Loss: 0.1612 | LR: 0.000010
+[2026-01-12 06:10:06] Epoch: 186 | Iter: 000186 | Step Time: 13.631s | Local Loss: 0.1393 | Avg Loss: 0.0495 | LR: 0.000010
+[2026-01-12 06:10:26] Epoch: 187 | Iter: 000187 | Step Time: 13.415s | Local Loss: 0.0135 | Avg Loss: 0.1909 | LR: 0.000010
+[2026-01-12 06:10:45] Epoch: 188 | Iter: 000188 | Step Time: 13.189s | Local Loss: 0.3146 | Avg Loss: 0.1427 | LR: 0.000010
+[2026-01-12 06:11:04] Epoch: 189 | Iter: 000189 | Step Time: 13.652s | Local Loss: 0.0603 | Avg Loss: 0.1282 | LR: 0.000010
+[2026-01-12 06:11:23] Epoch: 190 | Iter: 000190 | Step Time: 13.404s | Local Loss: 0.1194 | Avg Loss: 0.1480 | LR: 0.000010
+[2026-01-12 06:11:42] Epoch: 191 | Iter: 000191 | Step Time: 13.325s | Local Loss: 0.0286 | Avg Loss: 0.2219 | LR: 0.000010
+[2026-01-12 06:12:02] Epoch: 192 | Iter: 000192 | Step Time: 13.709s | Local Loss: 0.0426 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 06:12:21] Epoch: 193 | Iter: 000193 | Step Time: 13.550s | Local Loss: 0.0344 | Avg Loss: 0.1900 | LR: 0.000010
+[2026-01-12 06:12:40] Epoch: 194 | Iter: 000194 | Step Time: 13.511s | Local Loss: 0.0691 | Avg Loss: 0.0420 | LR: 0.000010
+[2026-01-12 06:12:59] Epoch: 195 | Iter: 000195 | Step Time: 13.124s | Local Loss: 0.2628 | Avg Loss: 0.1410 | LR: 0.000010
+[2026-01-12 06:13:18] Epoch: 196 | Iter: 000196 | Step Time: 13.285s | Local Loss: 0.0683 | Avg Loss: 0.0562 | LR: 0.000010
+[2026-01-12 06:13:38] Epoch: 197 | Iter: 000197 | Step Time: 13.767s | Local Loss: 0.1016 | Avg Loss: 0.1073 | LR: 0.000010
+[2026-01-12 06:13:57] Epoch: 198 | Iter: 000198 | Step Time: 13.768s | Local Loss: 0.0674 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 06:14:16] Epoch: 199 | Iter: 000199 | Step Time: 13.064s | Local Loss: 0.0656 | Avg Loss: 0.1294 | LR: 0.000010
+[2026-01-12 06:17:18] Epoch: 200 | Iter: 000200 | Step Time: 17.935s | Local Loss: 0.0473 | Avg Loss: 0.0367 | LR: 0.000010
+[2026-01-12 06:17:39] Epoch: 201 | Iter: 000201 | Step Time: 15.176s | Local Loss: 0.0283 | Avg Loss: 0.1298 | LR: 0.000010
+[2026-01-12 06:18:01] Epoch: 202 | Iter: 000202 | Step Time: 15.044s | Local Loss: 0.0175 | Avg Loss: 0.1005 | LR: 0.000010
+[2026-01-12 06:18:22] Epoch: 203 | Iter: 000203 | Step Time: 15.304s | Local Loss: 0.4028 | Avg Loss: 0.0558 | LR: 0.000010
+[2026-01-12 06:18:44] Epoch: 204 | Iter: 000204 | Step Time: 15.279s | Local Loss: 0.0312 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 06:19:05] Epoch: 205 | Iter: 000205 | Step Time: 14.977s | Local Loss: 0.0258 | Avg Loss: 0.0315 | LR: 0.000010
+[2026-01-12 06:19:26] Epoch: 206 | Iter: 000206 | Step Time: 15.421s | Local Loss: 0.0506 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:19:48] Epoch: 207 | Iter: 000207 | Step Time: 15.322s | Local Loss: 0.0187 | Avg Loss: 0.1571 | LR: 0.000010
+[2026-01-12 06:20:09] Epoch: 208 | Iter: 000208 | Step Time: 14.702s | Local Loss: 0.0456 | Avg Loss: 0.1217 | LR: 0.000010
+[2026-01-12 06:20:30] Epoch: 209 | Iter: 000209 | Step Time: 14.943s | Local Loss: 0.0265 | Avg Loss: 0.0292 | LR: 0.000010
+[2026-01-12 06:20:52] Epoch: 210 | Iter: 000210 | Step Time: 15.426s | Local Loss: 0.0301 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:21:13] Epoch: 211 | Iter: 000211 | Step Time: 15.038s | Local Loss: 0.1326 | Avg Loss: 0.0409 | LR: 0.000010
+[2026-01-12 06:21:34] Epoch: 212 | Iter: 000212 | Step Time: 14.771s | Local Loss: 0.0263 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 06:21:55] Epoch: 213 | Iter: 000213 | Step Time: 15.204s | Local Loss: 0.0375 | Avg Loss: 0.0381 | LR: 0.000010
+[2026-01-12 06:22:17] Epoch: 214 | Iter: 000214 | Step Time: 14.955s | Local Loss: 0.3003 | Avg Loss: 0.1539 | LR: 0.000010
+[2026-01-12 06:22:38] Epoch: 215 | Iter: 000215 | Step Time: 14.915s | Local Loss: 0.0277 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 06:22:59] Epoch: 216 | Iter: 000216 | Step Time: 14.424s | Local Loss: 0.0572 | Avg Loss: 0.0772 | LR: 0.000010
+[2026-01-12 06:23:22] Epoch: 217 | Iter: 000217 | Step Time: 16.382s | Local Loss: 0.2722 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:23:41] Epoch: 218 | Iter: 000218 | Step Time: 13.364s | Local Loss: 0.0212 | Avg Loss: 0.0722 | LR: 0.000010
+[2026-01-12 06:24:00] Epoch: 219 | Iter: 000219 | Step Time: 13.624s | Local Loss: 0.0691 | Avg Loss: 0.0410 | LR: 0.000010
+[2026-01-12 06:24:20] Epoch: 220 | Iter: 000220 | Step Time: 13.502s | Local Loss: 0.4143 | Avg Loss: 0.0639 | LR: 0.000010
+[2026-01-12 06:24:39] Epoch: 221 | Iter: 000221 | Step Time: 13.359s | Local Loss: 0.0404 | Avg Loss: 0.1347 | LR: 0.000010
+[2026-01-12 06:24:58] Epoch: 222 | Iter: 000222 | Step Time: 13.241s | Local Loss: 0.0695 | Avg Loss: 0.0905 | LR: 0.000010
+[2026-01-12 06:25:17] Epoch: 223 | Iter: 000223 | Step Time: 13.500s | Local Loss: 0.2295 | Avg Loss: 0.1586 | LR: 0.000010
+[2026-01-12 06:25:36] Epoch: 224 | Iter: 000224 | Step Time: 13.146s | Local Loss: 0.2101 | Avg Loss: 0.0301 | LR: 0.000010
+[2026-01-12 06:25:56] Epoch: 225 | Iter: 000225 | Step Time: 13.580s | Local Loss: 0.0950 | Avg Loss: 0.0434 | LR: 0.000010
+[2026-01-12 06:26:15] Epoch: 226 | Iter: 000226 | Step Time: 13.482s | Local Loss: 0.1817 | Avg Loss: 0.0548 | LR: 0.000010
+[2026-01-12 06:26:34] Epoch: 227 | Iter: 000227 | Step Time: 13.273s | Local Loss: 0.0148 | Avg Loss: 0.1462 | LR: 0.000010
+[2026-01-12 06:26:53] Epoch: 228 | Iter: 000228 | Step Time: 13.264s | Local Loss: 0.0418 | Avg Loss: 0.0912 | LR: 0.000010
+[2026-01-12 06:27:12] Epoch: 229 | Iter: 000229 | Step Time: 13.274s | Local Loss: 0.0222 | Avg Loss: 0.1520 | LR: 0.000010
+[2026-01-12 06:27:31] Epoch: 230 | Iter: 000230 | Step Time: 13.209s | Local Loss: 0.0632 | Avg Loss: 0.0345 | LR: 0.000010
+[2026-01-12 06:27:50] Epoch: 231 | Iter: 000231 | Step Time: 13.397s | Local Loss: 0.0230 | Avg Loss: 0.0466 | LR: 0.000010
+[2026-01-12 06:28:10] Epoch: 232 | Iter: 000232 | Step Time: 13.511s | Local Loss: 0.0223 | Avg Loss: 0.2427 | LR: 0.000010
+[2026-01-12 06:28:29] Epoch: 233 | Iter: 000233 | Step Time: 13.489s | Local Loss: 0.0277 | Avg Loss: 0.0933 | LR: 0.000010
+[2026-01-12 06:28:48] Epoch: 234 | Iter: 000234 | Step Time: 13.266s | Local Loss: 0.0285 | Avg Loss: 0.0987 | LR: 0.000010
+[2026-01-12 06:29:07] Epoch: 235 | Iter: 000235 | Step Time: 13.539s | Local Loss: 0.0240 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 06:29:27] Epoch: 236 | Iter: 000236 | Step Time: 13.325s | Local Loss: 0.1967 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 06:29:46] Epoch: 237 | Iter: 000237 | Step Time: 13.580s | Local Loss: 0.0083 | Avg Loss: 0.1621 | LR: 0.000010
+[2026-01-12 06:30:05] Epoch: 238 | Iter: 000238 | Step Time: 13.728s | Local Loss: 0.0327 | Avg Loss: 0.1628 | LR: 0.000010
+[2026-01-12 06:30:24] Epoch: 239 | Iter: 000239 | Step Time: 13.139s | Local Loss: 0.0937 | Avg Loss: 0.0748 | LR: 0.000010
+[2026-01-12 06:30:43] Epoch: 240 | Iter: 000240 | Step Time: 13.228s | Local Loss: 0.0799 | Avg Loss: 0.0835 | LR: 0.000010
+[2026-01-12 06:31:03] Epoch: 241 | Iter: 000241 | Step Time: 13.765s | Local Loss: 0.1058 | Avg Loss: 0.1148 | LR: 0.000010
+[2026-01-12 06:31:22] Epoch: 242 | Iter: 000242 | Step Time: 13.449s | Local Loss: 0.0687 | Avg Loss: 0.0272 | LR: 0.000010
+[2026-01-12 06:31:41] Epoch: 243 | Iter: 000243 | Step Time: 13.214s | Local Loss: 0.0866 | Avg Loss: 0.1115 | LR: 0.000010
+[2026-01-12 06:32:01] Epoch: 244 | Iter: 000244 | Step Time: 13.765s | Local Loss: 0.0539 | Avg Loss: 0.1065 | LR: 0.000010
+[2026-01-12 06:32:20] Epoch: 245 | Iter: 000245 | Step Time: 13.514s | Local Loss: 0.0265 | Avg Loss: 0.1187 | LR: 0.000010
+[2026-01-12 06:32:39] Epoch: 246 | Iter: 000246 | Step Time: 13.427s | Local Loss: 0.2014 | Avg Loss: 0.0918 | LR: 0.000010
+[2026-01-12 06:32:59] Epoch: 247 | Iter: 000247 | Step Time: 13.739s | Local Loss: 0.0213 | Avg Loss: 0.1131 | LR: 0.000010
+[2026-01-12 06:33:18] Epoch: 248 | Iter: 000248 | Step Time: 13.486s | Local Loss: 0.2594 | Avg Loss: 0.0614 | LR: 0.000010
+[2026-01-12 06:33:37] Epoch: 249 | Iter: 000249 | Step Time: 13.464s | Local Loss: 0.0201 | Avg Loss: 0.1176 | LR: 0.000010
+[2026-01-12 06:36:42] Epoch: 250 | Iter: 000250 | Step Time: 19.865s | Local Loss: 0.1546 | Avg Loss: 0.0490 | LR: 0.000010
+[2026-01-12 06:37:06] Epoch: 251 | Iter: 000251 | Step Time: 17.131s | Local Loss: 0.0230 | Avg Loss: 0.0608 | LR: 0.000010
+[2026-01-12 06:37:30] Epoch: 252 | Iter: 000252 | Step Time: 17.403s | Local Loss: 0.0645 | Avg Loss: 0.1409 | LR: 0.000010
+[2026-01-12 06:37:55] Epoch: 253 | Iter: 000253 | Step Time: 17.267s | Local Loss: 0.0499 | Avg Loss: 0.0500 | LR: 0.000010
+[2026-01-12 06:38:19] Epoch: 254 | Iter: 000254 | Step Time: 17.344s | Local Loss: 0.0220 | Avg Loss: 0.1495 | LR: 0.000010
+[2026-01-12 06:38:44] Epoch: 255 | Iter: 000255 | Step Time: 18.439s | Local Loss: 0.0240 | Avg Loss: 0.0730 | LR: 0.000010
+[2026-01-12 06:39:04] Epoch: 256 | Iter: 000256 | Step Time: 14.399s | Local Loss: 0.0569 | Avg Loss: 0.0767 | LR: 0.000010
+[2026-01-12 06:39:24] Epoch: 257 | Iter: 000257 | Step Time: 13.680s | Local Loss: 0.0511 | Avg Loss: 0.0976 | LR: 0.000010
+[2026-01-12 06:39:43] Epoch: 258 | Iter: 000258 | Step Time: 13.397s | Local Loss: 0.1903 | Avg Loss: 0.1903 | LR: 0.000010
+[2026-01-12 06:40:03] Epoch: 259 | Iter: 000259 | Step Time: 13.717s | Local Loss: 0.0127 | Avg Loss: 0.0525 | LR: 0.000010
+[2026-01-12 06:40:22] Epoch: 260 | Iter: 000260 | Step Time: 13.368s | Local Loss: 0.0467 | Avg Loss: 0.1464 | LR: 0.000010
+[2026-01-12 06:40:41] Epoch: 261 | Iter: 000261 | Step Time: 13.684s | Local Loss: 0.0147 | Avg Loss: 0.0448 | LR: 0.000010
+[2026-01-12 06:41:00] Epoch: 262 | Iter: 000262 | Step Time: 13.485s | Local Loss: 0.0338 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 06:41:20] Epoch: 263 | Iter: 000263 | Step Time: 13.928s | Local Loss: 0.1157 | Avg Loss: 0.0390 | LR: 0.000010
+[2026-01-12 06:41:39] Epoch: 264 | Iter: 000264 | Step Time: 13.526s | Local Loss: 0.1502 | Avg Loss: 0.1472 | LR: 0.000010
+[2026-01-12 06:41:58] Epoch: 265 | Iter: 000265 | Step Time: 13.305s | Local Loss: 0.0418 | Avg Loss: 0.0789 | LR: 0.000010
+[2026-01-12 06:42:17] Epoch: 266 | Iter: 000266 | Step Time: 13.195s | Local Loss: 0.0722 | Avg Loss: 0.0664 | LR: 0.000010
+[2026-01-12 06:42:38] Epoch: 267 | Iter: 000267 | Step Time: 14.678s | Local Loss: 0.0452 | Avg Loss: 0.1414 | LR: 0.000010
+[2026-01-12 06:42:56] Epoch: 268 | Iter: 000268 | Step Time: 13.219s | Local Loss: 0.0396 | Avg Loss: 0.0757 | LR: 0.000010
+[2026-01-12 06:43:15] Epoch: 269 | Iter: 000269 | Step Time: 13.206s | Local Loss: 0.0347 | Avg Loss: 0.1063 | LR: 0.000010
+[2026-01-12 06:43:35] Epoch: 270 | Iter: 000270 | Step Time: 13.448s | Local Loss: 0.0194 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 06:43:54] Epoch: 271 | Iter: 000271 | Step Time: 13.304s | Local Loss: 0.0210 | Avg Loss: 0.0352 | LR: 0.000010
+[2026-01-12 06:44:21] Epoch: 272 | Iter: 000272 | Step Time: 16.924s | Local Loss: 0.0329 | Avg Loss: 0.0554 | LR: 0.000010
+[2026-01-12 06:44:47] Epoch: 273 | Iter: 000273 | Step Time: 20.746s | Local Loss: 0.0281 | Avg Loss: 0.0576 | LR: 0.000010
+[2026-01-12 06:45:15] Epoch: 274 | Iter: 000274 | Step Time: 20.237s | Local Loss: 0.0790 | Avg Loss: 0.1080 | LR: 0.000010
+[2026-01-12 06:45:42] Epoch: 275 | Iter: 000275 | Step Time: 20.560s | Local Loss: 0.0330 | Avg Loss: 0.0455 | LR: 0.000010
+[2026-01-12 06:46:08] Epoch: 276 | Iter: 000276 | Step Time: 20.557s | Local Loss: 0.0588 | Avg Loss: 0.0451 | LR: 0.000010
+[2026-01-12 06:46:35] Epoch: 277 | Iter: 000277 | Step Time: 21.118s | Local Loss: 0.0433 | Avg Loss: 0.0667 | LR: 0.000010
+[2026-01-12 06:47:05] Epoch: 278 | Iter: 000278 | Step Time: 20.587s | Local Loss: 0.0233 | Avg Loss: 0.1211 | LR: 0.000010
+[2026-01-12 06:47:31] Epoch: 279 | Iter: 000279 | Step Time: 20.190s | Local Loss: 0.0263 | Avg Loss: 0.1438 | LR: 0.000010
+[2026-01-12 06:47:58] Epoch: 280 | Iter: 000280 | Step Time: 20.900s | Local Loss: 0.1254 | Avg Loss: 0.0494 | LR: 0.000010
+[2026-01-12 06:48:24] Epoch: 281 | Iter: 000281 | Step Time: 20.315s | Local Loss: 0.0162 | Avg Loss: 0.0163 | LR: 0.000010
+[2026-01-12 06:48:51] Epoch: 282 | Iter: 000282 | Step Time: 21.527s | Local Loss: 0.0316 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 06:49:17] Epoch: 283 | Iter: 000283 | Step Time: 20.208s | Local Loss: 0.0276 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 06:49:43] Epoch: 284 | Iter: 000284 | Step Time: 20.836s | Local Loss: 0.0669 | Avg Loss: 0.1156 | LR: 0.000010
+[2026-01-12 06:50:13] Epoch: 285 | Iter: 000285 | Step Time: 20.571s | Local Loss: 0.0109 | Avg Loss: 0.0359 | LR: 0.000010
+[2026-01-12 06:50:40] Epoch: 286 | Iter: 000286 | Step Time: 21.603s | Local Loss: 0.0071 | Avg Loss: 0.0864 | LR: 0.000010
+[2026-01-12 06:51:06] Epoch: 287 | Iter: 000287 | Step Time: 20.025s | Local Loss: 0.0126 | Avg Loss: 0.1394 | LR: 0.000010
+[2026-01-12 06:51:34] Epoch: 288 | Iter: 000288 | Step Time: 22.360s | Local Loss: 0.0544 | Avg Loss: 0.1221 | LR: 0.000010
+[2026-01-12 06:52:01] Epoch: 289 | Iter: 000289 | Step Time: 20.875s | Local Loss: 0.0680 | Avg Loss: 0.1045 | LR: 0.000010
+[2026-01-12 06:52:32] Epoch: 290 | Iter: 000290 | Step Time: 25.046s | Local Loss: 0.0186 | Avg Loss: 0.0274 | LR: 0.000010
+[2026-01-12 06:52:58] Epoch: 291 | Iter: 000291 | Step Time: 19.959s | Local Loss: 0.2174 | Avg Loss: 0.1162 | LR: 0.000010
+[2026-01-12 06:53:25] Epoch: 292 | Iter: 000292 | Step Time: 21.332s | Local Loss: 0.0403 | Avg Loss: 0.0289 | LR: 0.000010
+[2026-01-12 06:53:51] Epoch: 293 | Iter: 000293 | Step Time: 20.798s | Local Loss: 0.0433 | Avg Loss: 0.1367 | LR: 0.000010
+[2026-01-12 06:54:19] Epoch: 294 | Iter: 000294 | Step Time: 21.302s | Local Loss: 0.0585 | Avg Loss: 0.1697 | LR: 0.000010
+[2026-01-12 06:54:41] Epoch: 295 | Iter: 000295 | Step Time: 15.939s | Local Loss: 0.0373 | Avg Loss: 0.0258 | LR: 0.000010
+[2026-01-12 06:55:00] Epoch: 296 | Iter: 000296 | Step Time: 13.139s | Local Loss: 0.0683 | Avg Loss: 0.0823 | LR: 0.000010
+[2026-01-12 06:55:19] Epoch: 297 | Iter: 000297 | Step Time: 13.489s | Local Loss: 0.0795 | Avg Loss: 0.1374 | LR: 0.000010
+[2026-01-12 06:55:38] Epoch: 298 | Iter: 000298 | Step Time: 13.571s | Local Loss: 0.1466 | Avg Loss: 0.1119 | LR: 0.000010
+[2026-01-12 06:55:57] Epoch: 299 | Iter: 000299 | Step Time: 13.555s | Local Loss: 0.0216 | Avg Loss: 0.0234 | LR: 0.000010
+[2026-01-12 06:58:59] Epoch: 300 | Iter: 000300 | Step Time: 17.871s | Local Loss: 0.0183 | Avg Loss: 0.1091 | LR: 0.000010
+[2026-01-12 06:59:21] Epoch: 301 | Iter: 000301 | Step Time: 15.499s | Local Loss: 0.0390 | Avg Loss: 0.0829 | LR: 0.000010
+[2026-01-12 06:59:43] Epoch: 302 | Iter: 000302 | Step Time: 15.527s | Local Loss: 0.0460 | Avg Loss: 0.1800 | LR: 0.000010
+[2026-01-12 07:00:04] Epoch: 303 | Iter: 000303 | Step Time: 15.080s | Local Loss: 0.0354 | Avg Loss: 0.0471 | LR: 0.000010
+[2026-01-12 07:00:25] Epoch: 304 | Iter: 000304 | Step Time: 14.884s | Local Loss: 0.0224 | Avg Loss: 0.0761 | LR: 0.000010
+[2026-01-12 07:00:46] Epoch: 305 | Iter: 000305 | Step Time: 15.323s | Local Loss: 0.0109 | Avg Loss: 0.0690 | LR: 0.000010
+[2026-01-12 07:01:08] Epoch: 306 | Iter: 000306 | Step Time: 15.287s | Local Loss: 0.0040 | Avg Loss: 0.0122 | LR: 0.000010
+[2026-01-12 07:01:29] Epoch: 307 | Iter: 000307 | Step Time: 15.217s | Local Loss: 0.0122 | Avg Loss: 0.0588 | LR: 0.000010
+[2026-01-12 07:01:50] Epoch: 308 | Iter: 000308 | Step Time: 14.959s | Local Loss: 0.2726 | Avg Loss: 0.0975 | LR: 0.000010
+[2026-01-12 07:02:12] Epoch: 309 | Iter: 000309 | Step Time: 15.015s | Local Loss: 0.3181 | Avg Loss: 0.1650 | LR: 0.000010
+[2026-01-12 07:02:34] Epoch: 310 | Iter: 000310 | Step Time: 16.031s | Local Loss: 0.0000 | Avg Loss: 0.0534 | LR: 0.000010
+[2026-01-12 07:02:53] Epoch: 311 | Iter: 000311 | Step Time: 13.679s | Local Loss: 0.0453 | Avg Loss: 0.0185 | LR: 0.000010
+[2026-01-12 07:03:12] Epoch: 312 | Iter: 000312 | Step Time: 13.453s | Local Loss: 0.0263 | Avg Loss: 0.0594 | LR: 0.000010
+[2026-01-12 07:03:31] Epoch: 313 | Iter: 000313 | Step Time: 13.293s | Local Loss: 0.0084 | Avg Loss: 0.0281 | LR: 0.000010
+[2026-01-12 07:03:50] Epoch: 314 | Iter: 000314 | Step Time: 13.165s | Local Loss: 0.0228 | Avg Loss: 0.0320 | LR: 0.000010
+[2026-01-12 07:04:09] Epoch: 315 | Iter: 000315 | Step Time: 13.303s | Local Loss: 0.1028 | Avg Loss: 0.0851 | LR: 0.000010
+[2026-01-12 07:04:29] Epoch: 316 | Iter: 000316 | Step Time: 14.074s | Local Loss: 0.2485 | Avg Loss: 0.0346 | LR: 0.000010
+[2026-01-12 07:04:48] Epoch: 317 | Iter: 000317 | Step Time: 13.345s | Local Loss: 0.0649 | Avg Loss: 0.0833 | LR: 0.000010
+[2026-01-12 07:05:07] Epoch: 318 | Iter: 000318 | Step Time: 13.287s | Local Loss: 0.0158 | Avg Loss: 0.0256 | LR: 0.000010
+[2026-01-12 07:05:26] Epoch: 319 | Iter: 000319 | Step Time: 13.650s | Local Loss: 0.0297 | Avg Loss: 0.1087 | LR: 0.000010
+[2026-01-12 07:05:45] Epoch: 320 | Iter: 000320 | Step Time: 13.430s | Local Loss: 0.0278 | Avg Loss: 0.0567 | LR: 0.000010
+[2026-01-12 07:06:05] Epoch: 321 | Iter: 000321 | Step Time: 14.172s | Local Loss: 0.0262 | Avg Loss: 0.0386 | LR: 0.000010
+[2026-01-12 07:06:25] Epoch: 322 | Iter: 000322 | Step Time: 14.317s | Local Loss: 0.0121 | Avg Loss: 0.0160 | LR: 0.000010
+[2026-01-12 07:06:45] Epoch: 323 | Iter: 000323 | Step Time: 14.136s | Local Loss: 0.2779 | Avg Loss: 0.0291 | LR: 0.000010
+[2026-01-12 07:07:05] Epoch: 324 | Iter: 000324 | Step Time: 14.145s | Local Loss: 0.0180 | Avg Loss: 0.0505 | LR: 0.000010
+[2026-01-12 07:07:25] Epoch: 325 | Iter: 000325 | Step Time: 14.316s | Local Loss: 0.0501 | Avg Loss: 0.0177 | LR: 0.000010
+[2026-01-12 07:07:45] Epoch: 326 | Iter: 000326 | Step Time: 14.263s | Local Loss: 0.0098 | Avg Loss: 0.0540 | LR: 0.000010
+[2026-01-12 07:08:05] Epoch: 327 | Iter: 000327 | Step Time: 13.617s | Local Loss: 0.0878 | Avg Loss: 0.0312 | LR: 0.000010
+[2026-01-12 07:08:24] Epoch: 328 | Iter: 000328 | Step Time: 13.913s | Local Loss: 0.0169 | Avg Loss: 0.1384 | LR: 0.000010
+[2026-01-12 07:08:43] Epoch: 329 | Iter: 000329 | Step Time: 13.433s | Local Loss: 0.0435 | Avg Loss: 0.2363 | LR: 0.000010
+[2026-01-12 07:09:03] Epoch: 330 | Iter: 000330 | Step Time: 13.912s | Local Loss: 0.0157 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:09:22] Epoch: 331 | Iter: 000331 | Step Time: 13.554s | Local Loss: 0.0573 | Avg Loss: 0.0270 | LR: 0.000010
+[2026-01-12 07:09:42] Epoch: 332 | Iter: 000332 | Step Time: 13.817s | Local Loss: 0.0968 | Avg Loss: 0.0327 | LR: 0.000010
+[2026-01-12 07:10:01] Epoch: 333 | Iter: 000333 | Step Time: 13.711s | Local Loss: 0.0169 | Avg Loss: 0.0362 | LR: 0.000010
+[2026-01-12 07:10:21] Epoch: 334 | Iter: 000334 | Step Time: 13.657s | Local Loss: 0.0260 | Avg Loss: 0.0512 | LR: 0.000010
+[2026-01-12 07:10:40] Epoch: 335 | Iter: 000335 | Step Time: 13.570s | Local Loss: 0.0433 | Avg Loss: 0.0304 | LR: 0.000010
+[2026-01-12 07:11:00] Epoch: 336 | Iter: 000336 | Step Time: 14.099s | Local Loss: 0.0240 | Avg Loss: 0.0463 | LR: 0.000010
+[2026-01-12 07:11:19] Epoch: 337 | Iter: 000337 | Step Time: 13.378s | Local Loss: 0.1204 | Avg Loss: 0.0670 | LR: 0.000010
+[2026-01-12 07:11:38] Epoch: 338 | Iter: 000338 | Step Time: 13.313s | Local Loss: 0.0369 | Avg Loss: 0.0313 | LR: 0.000010
+[2026-01-12 07:11:58] Epoch: 339 | Iter: 000339 | Step Time: 14.094s | Local Loss: 0.0313 | Avg Loss: 0.0267 | LR: 0.000010
+[2026-01-12 07:12:17] Epoch: 340 | Iter: 000340 | Step Time: 13.284s | Local Loss: 0.0114 | Avg Loss: 0.0867 | LR: 0.000010
+[2026-01-12 07:12:36] Epoch: 341 | Iter: 000341 | Step Time: 13.328s | Local Loss: 0.0165 | Avg Loss: 0.0764 | LR: 0.000010
+[2026-01-12 07:12:55] Epoch: 342 | Iter: 000342 | Step Time: 13.463s | Local Loss: 0.0648 | Avg Loss: 0.0332 | LR: 0.000010
+[2026-01-12 07:13:14] Epoch: 343 | Iter: 000343 | Step Time: 13.643s | Local Loss: 0.0423 | Avg Loss: 0.0597 | LR: 0.000010
+[2026-01-12 07:13:34] Epoch: 344 | Iter: 000344 | Step Time: 13.996s | Local Loss: 0.0151 | Avg Loss: 0.0321 | LR: 0.000010
+[2026-01-12 07:13:54] Epoch: 345 | Iter: 000345 | Step Time: 14.184s | Local Loss: 0.0494 | Avg Loss: 0.0305 | LR: 0.000010
+[2026-01-12 07:14:13] Epoch: 346 | Iter: 000346 | Step Time: 13.687s | Local Loss: 0.0263 | Avg Loss: 0.1264 | LR: 0.000010
+[2026-01-12 07:14:33] Epoch: 347 | Iter: 000347 | Step Time: 14.109s | Local Loss: 0.1919 | Avg Loss: 0.0216 | LR: 0.000010
+[2026-01-12 07:14:52] Epoch: 348 | Iter: 000348 | Step Time: 13.596s | Local Loss: 0.0410 | Avg Loss: 0.0161 | LR: 0.000010
+[2026-01-12 07:15:11] Epoch: 349 | Iter: 000349 | Step Time: 13.573s | Local Loss: 0.0200 | Avg Loss: 0.0635 | LR: 0.000010
+[2026-01-12 07:18:16] Epoch: 350 | Iter: 000350 | Step Time: 19.862s | Local Loss: 0.0164 | Avg Loss: 0.0195 | LR: 0.000010
+[2026-01-12 07:18:40] Epoch: 351 | Iter: 000351 | Step Time: 17.485s | Local Loss: 0.2475 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:19:04] Epoch: 352 | Iter: 000352 | Step Time: 16.669s | Local Loss: 0.0254 | Avg Loss: 0.1018 | LR: 0.000010
+[2026-01-12 07:19:29] Epoch: 353 | Iter: 000353 | Step Time: 17.925s | Local Loss: 0.0356 | Avg Loss: 0.0215 | LR: 0.000010
+[2026-01-12 07:19:53] Epoch: 354 | Iter: 000354 | Step Time: 16.858s | Local Loss: 0.0539 | Avg Loss: 0.0182 | LR: 0.000010
+[2026-01-12 07:20:19] Epoch: 355 | Iter: 000355 | Step Time: 18.865s | Local Loss: 0.0080 | Avg Loss: 0.0506 | LR: 0.000010
+[2026-01-12 07:20:43] Epoch: 356 | Iter: 000356 | Step Time: 17.056s | Local Loss: 0.0106 | Avg Loss: 0.0429 | LR: 0.000010
+[2026-01-12 07:21:08] Epoch: 357 | Iter: 000357 | Step Time: 18.086s | Local Loss: 0.0362 | Avg Loss: 0.1244 | LR: 0.000010
+[2026-01-12 07:21:32] Epoch: 358 | Iter: 000358 | Step Time: 17.192s | Local Loss: 0.2045 | Avg Loss: 0.0335 | LR: 0.000010
+[2026-01-12 07:21:56] Epoch: 359 | Iter: 000359 | Step Time: 17.523s | Local Loss: 0.0137 | Avg Loss: 0.0510 | LR: 0.000010
+[2026-01-12 07:22:21] Epoch: 360 | Iter: 000360 | Step Time: 17.837s | Local Loss: 0.0096 | Avg Loss: 0.0623 | LR: 0.000010
+[2026-01-12 07:22:47] Epoch: 361 | Iter: 000361 | Step Time: 17.546s | Local Loss: 0.0132 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:23:10] Epoch: 362 | Iter: 000362 | Step Time: 16.796s | Local Loss: 0.2236 | Avg Loss: 0.0419 | LR: 0.000010
+[2026-01-12 07:23:42] Epoch: 363 | Iter: 000363 | Step Time: 25.087s | Local Loss: 0.0180 | Avg Loss: 0.0522 | LR: 0.000010
+[2026-01-12 07:24:10] Epoch: 364 | Iter: 000364 | Step Time: 20.237s | Local Loss: 0.0371 | Avg Loss: 0.0391 | LR: 0.000010
+[2026-01-12 07:24:36] Epoch: 365 | Iter: 000365 | Step Time: 18.729s | Local Loss: 0.0303 | Avg Loss: 0.0283 | LR: 0.000010
+[2026-01-12 07:24:56] Epoch: 366 | Iter: 000366 | Step Time: 14.164s | Local Loss: 0.0567 | Avg Loss: 0.0841 | LR: 0.000010
+[2026-01-12 07:25:15] Epoch: 367 | Iter: 000367 | Step Time: 13.415s | Local Loss: 0.0190 | Avg Loss: 0.0712 | LR: 0.000010
+[2026-01-12 07:25:33] Epoch: 368 | Iter: 000368 | Step Time: 12.930s | Local Loss: 0.0199 | Avg Loss: 0.0189 | LR: 0.000010
+[2026-01-12 07:25:52] Epoch: 369 | Iter: 000369 | Step Time: 13.064s | Local Loss: 0.0391 | Avg Loss: 0.1259 | LR: 0.000010
+[2026-01-12 07:26:11] Epoch: 370 | Iter: 000370 | Step Time: 13.106s | Local Loss: 0.0213 | Avg Loss: 0.0546 | LR: 0.000010
+[2026-01-12 07:26:29] Epoch: 371 | Iter: 000371 | Step Time: 12.444s | Local Loss: 0.0163 | Avg Loss: 0.0625 | LR: 0.000010
+[2026-01-12 07:26:48] Epoch: 372 | Iter: 000372 | Step Time: 13.124s | Local Loss: 0.2387 | Avg Loss: 0.0276 | LR: 0.000010
+[2026-01-12 07:27:06] Epoch: 373 | Iter: 000373 | Step Time: 12.766s | Local Loss: 0.0169 | Avg Loss: 0.0164 | LR: 0.000010
+[2026-01-12 07:27:25] Epoch: 374 | Iter: 000374 | Step Time: 12.662s | Local Loss: 0.0183 | Avg Loss: 0.0870 | LR: 0.000010
+[2026-01-12 07:27:44] Epoch: 375 | Iter: 000375 | Step Time: 13.150s | Local Loss: 0.0192 | Avg Loss: 0.0780 | LR: 0.000010
+[2026-01-12 07:28:02] Epoch: 376 | Iter: 000376 | Step Time: 12.744s | Local Loss: 0.0000 | Avg Loss: 0.0255 | LR: 0.000010
+[2026-01-12 07:28:21] Epoch: 377 | Iter: 000377 | Step Time: 13.129s | Local Loss: 0.0128 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:28:40] Epoch: 378 | Iter: 000378 | Step Time: 13.225s | Local Loss: 0.0273 | Avg Loss: 0.0271 | LR: 0.000010
+[2026-01-12 07:28:58] Epoch: 379 | Iter: 000379 | Step Time: 12.614s | Local Loss: 0.0180 | Avg Loss: 0.0158 | LR: 0.000010
+[2026-01-12 07:29:17] Epoch: 380 | Iter: 000380 | Step Time: 12.943s | Local Loss: 0.0131 | Avg Loss: 0.0104 | LR: 0.000010
+[2026-01-12 07:29:36] Epoch: 381 | Iter: 000381 | Step Time: 12.861s | Local Loss: 0.0752 | Avg Loss: 0.0423 | LR: 0.000010
+[2026-01-12 07:29:54] Epoch: 382 | Iter: 000382 | Step Time: 13.045s | Local Loss: 0.0135 | Avg Loss: 0.0557 | LR: 0.000010
+[2026-01-12 07:30:13] Epoch: 383 | Iter: 000383 | Step Time: 12.781s | Local Loss: 0.0154 | Avg Loss: 0.0395 | LR: 0.000010
+[2026-01-12 07:30:31] Epoch: 384 | Iter: 000384 | Step Time: 12.788s | Local Loss: 0.1679 | Avg Loss: 0.0432 | LR: 0.000010
+[2026-01-12 07:30:50] Epoch: 385 | Iter: 000385 | Step Time: 13.134s | Local Loss: 0.0810 | Avg Loss: 0.0428 | LR: 0.000010
+[2026-01-12 07:31:08] Epoch: 386 | Iter: 000386 | Step Time: 12.570s | Local Loss: 0.0415 | Avg Loss: 0.0620 | LR: 0.000010
+[2026-01-12 07:31:27] Epoch: 387 | Iter: 000387 | Step Time: 12.994s | Local Loss: 0.1228 | Avg Loss: 0.0239 | LR: 0.000010
+[2026-01-12 07:31:45] Epoch: 388 | Iter: 000388 | Step Time: 12.863s | Local Loss: 0.0268 | Avg Loss: 0.1071 | LR: 0.000010
+[2026-01-12 07:32:04] Epoch: 389 | Iter: 000389 | Step Time: 13.008s | Local Loss: 0.3066 | Avg Loss: 0.0309 | LR: 0.000010
+[2026-01-12 07:32:23] Epoch: 390 | Iter: 000390 | Step Time: 13.059s | Local Loss: 0.0345 | Avg Loss: 0.0853 | LR: 0.000010
+[2026-01-12 07:32:42] Epoch: 391 | Iter: 000391 | Step Time: 13.637s | Local Loss: 0.0065 | Avg Loss: 0.0914 | LR: 0.000010
+[2026-01-12 07:33:04] Epoch: 392 | Iter: 000392 | Step Time: 16.107s | Local Loss: 0.0323 | Avg Loss: 0.0156 | LR: 0.000010
+[2026-01-12 07:33:24] Epoch: 393 | Iter: 000393 | Step Time: 13.994s | Local Loss: 0.0083 | Avg Loss: 0.0998 | LR: 0.000010
+[2026-01-12 07:33:43] Epoch: 394 | Iter: 000394 | Step Time: 14.025s | Local Loss: 0.2096 | Avg Loss: 0.0968 | LR: 0.000010
+[2026-01-12 07:34:02] Epoch: 395 | Iter: 000395 | Step Time: 13.122s | Local Loss: 0.0206 | Avg Loss: 0.0287 | LR: 0.000010
+[2026-01-12 07:34:22] Epoch: 396 | Iter: 000396 | Step Time: 14.209s | Local Loss: 0.0675 | Avg Loss: 0.0316 | LR: 0.000010
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/training_loss_plots/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/loss_mean.png b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/training_loss_plots/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/loss_mean.png
new file mode 100644
index 0000000000000000000000000000000000000000..f2b47bed2a68a8b1a38c7f5bf4ef1570fdd87829
Binary files /dev/null and b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/training_loss_plots/Wan2.2_5B-Multi_view-normal_rope_480_832-3ref_local_1.12.1/loss_mean.png differ
diff --git a/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_736_1312-3ref/log/multi-view.yaml b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_736_1312-3ref/log/multi-view.yaml
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index 0000000000000000000000000000000000000000..22b7908aad30429d638c84eaca2bddbb7c65cdbf
--- /dev/null
+++ b/multi_view/ckpts/Wan2.2_5B-Multi_view-normal_rope_736_1312-3ref/log/multi-view.yaml
@@ -0,0 +1,31 @@
+train_args:
+  max_checkpoints_to_keep: 3
+  resume_from_checkpoint: True
+  seed: 42
+  save_steps: 150
+  save_epoches: 1
+  batch_size: 8
+  visual_log_project_name: Wan2.2_5B-Multi_view-normal_rope_480_832-3ref
+  output_path: /root/paddlejob/workspace/qizipeng/baidu/personal-code/Multi-view/multi_view/ckpts
+  local_model_path: /root/paddlejob/workspace/qizipeng/wanx_pretrainedmodels
+  zero_face_ratio: 0.1
+  split_rope: False
+  split1: False
+  split2: False
+  split3: False
+
+infer_args:
+  infer_step: 2700
+  epoch_id: 33
+
+  
+dataset_args:
+  base_path: /root/paddlejob/workspace/qizipeng/baidu/personal-code/Multi-view/multi_view/datasets/merge_dianshang_face_dete_angle_1637_230.json
+  num_frames: 81
+  height: 480 #736
+  width: 832 #1312
+  ref_num: 3 
+
+
+  # Wan2.2_5B-Multi_view-split_rope_384_640-3ref
+  # Wan2.2_5B-Multi_view-normal_rope_384_640-3ref
\ No newline at end of file