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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,60 @@
+---
+library_name: transformers
+license: other
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- llama-factory
+- freeze
+- generated_from_trainer
+model-index:
+- name: llama_under8_nsx
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# llama_under8_nsx
+
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the codes_nsx_under8 dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 3
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 384
+- total_eval_batch_size: 24
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- num_epochs: 1.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.48.2
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

all_results.json

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+{
+    "epoch": 0.9780564263322884,
+    "num_input_tokens_seen": 61341696,
+    "total_flos": 2.7621888463963423e+18,
+    "train_loss": 1.053089643136049,
+    "train_runtime": 6598.8894,
+    "train_samples_per_second": 2.316,
+    "train_steps_per_second": 0.006
+}

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 1.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 131072,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.48.2"
+}

llamaboard_config.yaml

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+top.booster: liger_kernel
+top.checkpoint_path: null
+top.finetuning_type: freeze
+top.model_name: Llama-3.1-8B-Instruct
+top.quantization_bit: none
+top.quantization_method: bitsandbytes
+top.rope_scaling: llama3
+top.template: llama3
+train.additional_target: ''
+train.apollo_rank: 256
+train.apollo_scale: 1
+train.apollo_target: all
+train.apollo_update_interval: 200
+train.badam_mode: layer
+train.badam_switch_interval: 50
+train.badam_switch_mode: ascending
+train.badam_update_ratio: 0.05
+train.batch_size: 16
+train.compute_type: bf16
+train.create_new_adapter: false
+train.cutoff_len: 4096
+train.dataset:
+- codes_nsx_under8
+train.dataset_dir: data
+train.ds_offload: false
+train.ds_stage: none
+train.extra_args: '{}'
+train.freeze_extra_modules: ''
+train.freeze_trainable_layers: 2
+train.freeze_trainable_modules: all
+train.galore_rank: 16
+train.galore_scale: 2
+train.galore_target: all
+train.galore_update_interval: 200
+train.gradient_accumulation_steps: 8
+train.learning_rate: 5e-5
+train.logging_steps: 1
+train.lora_alpha: 16
+train.lora_dropout: 0
+train.lora_rank: 8
+train.lora_target: ''
+train.loraplus_lr_ratio: 0
+train.lr_scheduler_type: cosine
+train.mask_history: false
+train.max_grad_norm: '1.0'
+train.max_samples: '50000000'
+train.neat_packing: true
+train.neftune_alpha: 0
+train.num_train_epochs: '1'
+train.packing: true
+train.ppo_score_norm: false
+train.ppo_whiten_rewards: false
+train.pref_beta: 0.1
+train.pref_ftx: 0
+train.pref_loss: sigmoid
+train.report_to:
+- none
+train.resize_vocab: false
+train.reward_model: null
+train.save_steps: 1000
+train.swanlab_api_key: ''
+train.swanlab_mode: cloud
+train.swanlab_project: llamafactory
+train.swanlab_run_name: ''
+train.swanlab_workspace: ''
+train.train_on_prompt: false
+train.training_stage: Supervised Fine-Tuning
+train.use_apollo: true
+train.use_badam: false
+train.use_dora: false
+train.use_galore: false
+train.use_llama_pro: true
+train.use_pissa: false
+train.use_rslora: false
+train.use_swanlab: false
+train.val_size: 0
+train.warmup_steps: 0

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+}

running_log.txt

@@ -0,0 +1,300 @@
+[INFO|2025-07-09 14:04:48] configuration_utils.py:696 >> loading configuration file config.json from cache at /home/kiho/.cache/huggingface/hub/models--meta-llama--Llama-3.1-8B-Instruct/snapshots/0e9e39f249a16976918f6564b8830bc894c89659/config.json
+
+[INFO|2025-07-09 14:04:48] configuration_utils.py:768 >> Model config LlamaConfig {
+  "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}
+
+
+[INFO|2025-07-09 14:04:48] tokenization_utils_base.py:2034 >> loading file tokenizer.json from cache at /home/kiho/.cache/huggingface/hub/models--meta-llama--Llama-3.1-8B-Instruct/snapshots/0e9e39f249a16976918f6564b8830bc894c89659/tokenizer.json
+
+[INFO|2025-07-09 14:04:48] tokenization_utils_base.py:2034 >> loading file tokenizer.model from cache at None
+
+[INFO|2025-07-09 14:04:48] tokenization_utils_base.py:2034 >> loading file added_tokens.json from cache at None
+
+[INFO|2025-07-09 14:04:48] tokenization_utils_base.py:2034 >> loading file special_tokens_map.json from cache at /home/kiho/.cache/huggingface/hub/models--meta-llama--Llama-3.1-8B-Instruct/snapshots/0e9e39f249a16976918f6564b8830bc894c89659/special_tokens_map.json
+
+[INFO|2025-07-09 14:04:48] tokenization_utils_base.py:2034 >> loading file tokenizer_config.json from cache at /home/kiho/.cache/huggingface/hub/models--meta-llama--Llama-3.1-8B-Instruct/snapshots/0e9e39f249a16976918f6564b8830bc894c89659/tokenizer_config.json
+
+[INFO|2025-07-09 14:04:48] tokenization_utils_base.py:2034 >> loading file chat_template.jinja from cache at None
+
+[INFO|2025-07-09 14:04:49] tokenization_utils_base.py:2304 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+
+[INFO|2025-07-09 14:04:49] logging.py:157 >> Add pad token: <|eot_id|>
+
+[INFO|2025-07-09 14:04:49] logging.py:157 >> Add <|eot_id|>,<|eom_id|> to stop words.
+
+[INFO|2025-07-09 14:04:49] logging.py:157 >> Loading dataset Codes3_query_filtered_553474_mark_less_than_8.0.json...
+
+[INFO|2025-07-09 14:05:11] configuration_utils.py:696 >> loading configuration file config.json from cache at /home/kiho/.cache/huggingface/hub/models--meta-llama--Llama-3.1-8B-Instruct/snapshots/0e9e39f249a16976918f6564b8830bc894c89659/config.json
+
+[INFO|2025-07-09 14:05:11] configuration_utils.py:768 >> Model config LlamaConfig {
+  "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}
+
+
+[WARNING|2025-07-09 14:05:11] logging.py:162 >> Input length is smaller than max length. Consider increase input length.
+
+[INFO|2025-07-09 14:05:11] logging.py:157 >> Using llama3 scaling strategy and setting scaling factor to 1.0.
+
+[INFO|2025-07-09 14:05:11] logging.py:157 >> Using block diagonal attention for sequence packing without cross-attention.
+
+[INFO|2025-07-09 14:05:11] logging.py:157 >> Liger kernel has been applied to the model.
+
+[INFO|2025-07-09 14:05:11] modeling_utils.py:3904 >> loading weights file model.safetensors from cache at /home/kiho/.cache/huggingface/hub/models--meta-llama--Llama-3.1-8B-Instruct/snapshots/0e9e39f249a16976918f6564b8830bc894c89659/model.safetensors.index.json
+
+[INFO|2025-07-09 14:05:11] modeling_utils.py:1582 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
+
+[INFO|2025-07-09 14:05:11] configuration_utils.py:1140 >> Generate config GenerationConfig {
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ]
+}
+
+
+[INFO|2025-07-09 14:05:15] modeling_utils.py:4888 >> All model checkpoint weights were used when initializing LlamaForCausalLM.
+
+
+[INFO|2025-07-09 14:05:15] modeling_utils.py:4896 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at meta-llama/Llama-3.1-8B-Instruct.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
+
+[INFO|2025-07-09 14:05:16] configuration_utils.py:1095 >> loading configuration file generation_config.json from cache at /home/kiho/.cache/huggingface/hub/models--meta-llama--Llama-3.1-8B-Instruct/snapshots/0e9e39f249a16976918f6564b8830bc894c89659/generation_config.json
+
+[INFO|2025-07-09 14:05:16] configuration_utils.py:1140 >> Generate config GenerationConfig {
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9
+}
+
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> Gradient checkpointing enabled.
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> Using torch SDPA for faster training and inference.
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> Upcasting trainable params to float32.
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> Fine-tuning method: Freeze
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> Set trainable layers: .15.,.31.
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> trainable params: 436,224,000 || all params: 8,030,261,248 || trainable%: 5.4323
+
+[INFO|2025-07-09 14:05:16] trainer.py:741 >> Using auto half precision backend
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> Found linear modules: gate_proj,o_proj,q_proj,v_proj,up_proj,down_proj,k_proj
+
+[INFO|2025-07-09 14:05:16] logging.py:157 >> Using APOLLO optimizer with args: {'rank': 256, 'proj': 'random', 'proj_type': 'std', 'update_proj_gap': 200, 'scale': 1, 'scale_type': 'channel', 'scale_front': False}.
+
+[INFO|2025-07-09 14:05:16] trainer.py:2369 >> ***** Running training *****
+
+[INFO|2025-07-09 14:05:16] trainer.py:2370 >>   Num examples = 15,286
+
+[INFO|2025-07-09 14:05:16] trainer.py:2371 >>   Num Epochs = 1
+
+[INFO|2025-07-09 14:05:16] trainer.py:2372 >>   Instantaneous batch size per device = 16
+
+[INFO|2025-07-09 14:05:16] trainer.py:2375 >>   Total train batch size (w. parallel, distributed & accumulation) = 384
+
+[INFO|2025-07-09 14:05:16] trainer.py:2376 >>   Gradient Accumulation steps = 8
+
+[INFO|2025-07-09 14:05:16] trainer.py:2377 >>   Total optimization steps = 39
+
+[INFO|2025-07-09 14:05:16] trainer.py:2378 >>   Number of trainable parameters = 436,224,000
+
+[INFO|2025-07-09 14:08:11] logging.py:157 >> {'loss': 1.5881, 'learning_rate': 4.9919e-05, 'epoch': 0.03, 'throughput': 9060.58}
+
+[INFO|2025-07-09 14:10:59] logging.py:157 >> {'loss': 1.2787, 'learning_rate': 4.9676e-05, 'epoch': 0.05, 'throughput': 9206.10}
+
+[INFO|2025-07-09 14:13:47] logging.py:157 >> {'loss': 1.1866, 'learning_rate': 4.9274e-05, 'epoch': 0.08, 'throughput': 9254.84}
+
+[INFO|2025-07-09 14:16:35] logging.py:157 >> {'loss': 1.1526, 'learning_rate': 4.8713e-05, 'epoch': 0.10, 'throughput': 9284.18}
+
+[INFO|2025-07-09 14:19:23] logging.py:157 >> {'loss': 1.1538, 'learning_rate': 4.7999e-05, 'epoch': 0.13, 'throughput': 9300.46}
+
+[INFO|2025-07-09 14:22:11] logging.py:157 >> {'loss': 1.0872, 'learning_rate': 4.7136e-05, 'epoch': 0.15, 'throughput': 9312.02}
+
+[INFO|2025-07-09 14:24:58] logging.py:157 >> {'loss': 1.0878, 'learning_rate': 4.6130e-05, 'epoch': 0.18, 'throughput': 9320.35}
+
+[INFO|2025-07-09 14:27:46] logging.py:157 >> {'loss': 1.0683, 'learning_rate': 4.4986e-05, 'epoch': 0.20, 'throughput': 9326.64}
+
+[INFO|2025-07-09 14:30:34] logging.py:157 >> {'loss': 1.0934, 'learning_rate': 4.3713e-05, 'epoch': 0.23, 'throughput': 9331.22}
+
+[INFO|2025-07-09 14:33:22] logging.py:157 >> {'loss': 1.0793, 'learning_rate': 4.2318e-05, 'epoch': 0.25, 'throughput': 9335.80}
+
+[INFO|2025-07-09 14:36:11] logging.py:157 >> {'loss': 1.0490, 'learning_rate': 4.0811e-05, 'epoch': 0.28, 'throughput': 9334.55}
+
+[INFO|2025-07-09 14:38:59] logging.py:157 >> {'loss': 1.0616, 'learning_rate': 3.9202e-05, 'epoch': 0.30, 'throughput': 9334.57}
+
+[INFO|2025-07-09 14:41:47] logging.py:157 >> {'loss': 1.0608, 'learning_rate': 3.7500e-05, 'epoch': 0.33, 'throughput': 9337.69}
+
+[INFO|2025-07-09 14:44:35] logging.py:157 >> {'loss': 1.0509, 'learning_rate': 3.5717e-05, 'epoch': 0.35, 'throughput': 9339.57}
+
+[INFO|2025-07-09 14:47:23] logging.py:157 >> {'loss': 1.0264, 'learning_rate': 3.3865e-05, 'epoch': 0.38, 'throughput': 9341.52}
+
+[INFO|2025-07-09 14:50:11] logging.py:157 >> {'loss': 1.0232, 'learning_rate': 3.1955e-05, 'epoch': 0.40, 'throughput': 9342.17}
+
+[INFO|2025-07-09 14:52:59] logging.py:157 >> {'loss': 1.0238, 'learning_rate': 3.0001e-05, 'epoch': 0.43, 'throughput': 9343.32}
+
+[INFO|2025-07-09 14:55:47] logging.py:157 >> {'loss': 1.0305, 'learning_rate': 2.8013e-05, 'epoch': 0.45, 'throughput': 9343.77}
+
+[INFO|2025-07-09 14:58:35] logging.py:157 >> {'loss': 1.0035, 'learning_rate': 2.6007e-05, 'epoch': 0.48, 'throughput': 9344.17}
+
+[INFO|2025-07-09 15:01:24] logging.py:157 >> {'loss': 0.9984, 'learning_rate': 2.3993e-05, 'epoch': 0.50, 'throughput': 9344.35}
+
+[INFO|2025-07-09 15:04:12] logging.py:157 >> {'loss': 1.0343, 'learning_rate': 2.1987e-05, 'epoch': 0.53, 'throughput': 9344.46}
+
+[INFO|2025-07-09 15:07:00] logging.py:157 >> {'loss': 1.0030, 'learning_rate': 1.9999e-05, 'epoch': 0.55, 'throughput': 9344.78}
+
+[INFO|2025-07-09 15:09:49] logging.py:157 >> {'loss': 0.9702, 'learning_rate': 1.8045e-05, 'epoch': 0.58, 'throughput': 9344.47}
+
+[INFO|2025-07-09 15:12:37] logging.py:157 >> {'loss': 1.0174, 'learning_rate': 1.6135e-05, 'epoch': 0.60, 'throughput': 9344.52}
+
+[INFO|2025-07-09 15:15:25] logging.py:157 >> {'loss': 1.0035, 'learning_rate': 1.4283e-05, 'epoch': 0.63, 'throughput': 9344.95}
+
+[INFO|2025-07-09 15:18:14] logging.py:157 >> {'loss': 1.0231, 'learning_rate': 1.2500e-05, 'epoch': 0.65, 'throughput': 9344.23}
+
+[INFO|2025-07-09 15:21:02] logging.py:157 >> {'loss': 0.9964, 'learning_rate': 1.0798e-05, 'epoch': 0.68, 'throughput': 9343.35}
+
+[INFO|2025-07-09 15:23:51] logging.py:157 >> {'loss': 0.9719, 'learning_rate': 9.1889e-06, 'epoch': 0.70, 'throughput': 9342.55}
+
+[INFO|2025-07-09 15:26:40] logging.py:157 >> {'loss': 0.9950, 'learning_rate': 7.6819e-06, 'epoch': 0.73, 'throughput': 9341.68}
+
+[INFO|2025-07-09 15:29:29] logging.py:157 >> {'loss': 1.0011, 'learning_rate': 6.2872e-06, 'epoch': 0.75, 'throughput': 9340.67}
+
+[INFO|2025-07-09 15:32:18] logging.py:157 >> {'loss': 1.0124, 'learning_rate': 5.0139e-06, 'epoch': 0.78, 'throughput': 9339.95}
+
+[INFO|2025-07-09 15:35:06] logging.py:157 >> {'loss': 0.9797, 'learning_rate': 3.8702e-06, 'epoch': 0.80, 'throughput': 9339.24}
+
+[INFO|2025-07-09 15:37:55] logging.py:157 >> {'loss': 0.9685, 'learning_rate': 2.8636e-06, 'epoch': 0.83, 'throughput': 9338.37}
+
+[INFO|2025-07-09 15:40:44] logging.py:157 >> {'loss': 1.0069, 'learning_rate': 2.0005e-06, 'epoch': 0.85, 'throughput': 9337.35}
+
+[INFO|2025-07-09 15:43:33] logging.py:157 >> {'loss': 0.9941, 'learning_rate': 1.2866e-06, 'epoch': 0.88, 'throughput': 9336.82}
+
+[INFO|2025-07-09 15:46:22] logging.py:157 >> {'loss': 1.0230, 'learning_rate': 7.2645e-07, 'epoch': 0.90, 'throughput': 9335.84}
+
+[INFO|2025-07-09 15:49:11] logging.py:157 >> {'loss': 0.9832, 'learning_rate': 3.2374e-07, 'epoch': 0.93, 'throughput': 9335.02}
+
+[INFO|2025-07-09 15:52:00] logging.py:157 >> {'loss': 0.9791, 'learning_rate': 8.1067e-08, 'epoch': 0.95, 'throughput': 9334.12}
+
+[INFO|2025-07-09 15:54:50] logging.py:157 >> {'loss': 1.0038, 'learning_rate': 0.0000e+00, 'epoch': 0.98, 'throughput': 9333.23}
+
+[INFO|2025-07-09 15:54:50] trainer.py:3910 >> Saving model checkpoint to saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/checkpoint-39
+
+[INFO|2025-07-09 15:54:50] configuration_utils.py:420 >> Configuration saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/checkpoint-39/config.json
+
+[INFO|2025-07-09 15:54:50] configuration_utils.py:909 >> Configuration saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/checkpoint-39/generation_config.json
+
+[INFO|2025-07-09 15:55:14] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/checkpoint-39/model.safetensors.index.json.
+
+[INFO|2025-07-09 15:55:14] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/checkpoint-39/tokenizer_config.json
+
+[INFO|2025-07-09 15:55:14] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/checkpoint-39/special_tokens_map.json
+
+[INFO|2025-07-09 15:55:15] trainer.py:2643 >> 
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
+[INFO|2025-07-09 15:55:15] trainer.py:3910 >> Saving model checkpoint to saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx
+
+[INFO|2025-07-09 15:55:15] configuration_utils.py:420 >> Configuration saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/config.json
+
+[INFO|2025-07-09 15:55:15] configuration_utils.py:909 >> Configuration saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/generation_config.json
+
+[INFO|2025-07-09 15:55:40] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/model.safetensors.index.json.
+
+[INFO|2025-07-09 15:55:40] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/tokenizer_config.json
+
+[INFO|2025-07-09 15:55:40] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/Llama-3.1-8B-Instruct/freeze/llama_under8_nsx/special_tokens_map.json
+
+[WARNING|2025-07-09 15:55:41] logging.py:162 >> No metric eval_loss to plot.
+
+[WARNING|2025-07-09 15:55:41] logging.py:162 >> No metric eval_accuracy to plot.
+
+[INFO|2025-07-09 15:55:41] modelcard.py:449 >> Dropping the following result as it does not have all the necessary fields:
+{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
+

special_tokens_map.json

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+    },
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+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
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+    },
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+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
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+      "rstrip": false,
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+    },
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+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
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+    },
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+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
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+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
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+      "content": "<|reserved_special_token_241|>",
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+    },
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+      "lstrip": false,
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+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
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+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|eot_id|>",
+    "<|eom_id|>"
+  ],
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 4096,
+  "pad_token": "<|eot_id|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

train_results.json

@@ -0,0 +1,9 @@
+{
+    "epoch": 0.9780564263322884,
+    "num_input_tokens_seen": 61341696,
+    "total_flos": 2.7621888463963423e+18,
+    "train_loss": 1.053089643136049,
+    "train_runtime": 6598.8894,
+    "train_samples_per_second": 2.316,
+    "train_steps_per_second": 0.006
+}

trainer_log.jsonl

@@ -0,0 +1,40 @@
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+{"current_steps": 2, "total_steps": 39, "loss": 1.2787, "lr": 4.967625656594782e-05, "epoch": 0.050156739811912224, "percentage": 5.13, "elapsed_time": "0:05:41", "remaining_time": "1:45:21", "throughput": 9206.1, "total_tokens": 3145728}
+{"current_steps": 3, "total_steps": 39, "loss": 1.1866, "lr": 4.92735454356513e-05, "epoch": 0.07523510971786834, "percentage": 7.69, "elapsed_time": "0:08:29", "remaining_time": "1:41:58", "throughput": 9254.84, "total_tokens": 4718592}
+{"current_steps": 4, "total_steps": 39, "loss": 1.1526, "lr": 4.8713411048678635e-05, "epoch": 0.10031347962382445, "percentage": 10.26, "elapsed_time": "0:11:17", "remaining_time": "1:38:49", "throughput": 9284.18, "total_tokens": 6291456}
+{"current_steps": 5, "total_steps": 39, "loss": 1.1538, "lr": 4.799948609147061e-05, "epoch": 0.12539184952978055, "percentage": 12.82, "elapsed_time": "0:14:05", "remaining_time": "1:35:49", "throughput": 9300.46, "total_tokens": 7864320}
+{"current_steps": 6, "total_steps": 39, "loss": 1.0872, "lr": 4.713640064133025e-05, "epoch": 0.15047021943573669, "percentage": 15.38, "elapsed_time": "0:16:53", "remaining_time": "1:32:53", "throughput": 9312.02, "total_tokens": 9437184}
+{"current_steps": 7, "total_steps": 39, "loss": 1.0878, "lr": 4.6129752138594874e-05, "epoch": 0.1755485893416928, "percentage": 17.95, "elapsed_time": "0:19:41", "remaining_time": "1:30:00", "throughput": 9320.35, "total_tokens": 11010048}
+{"current_steps": 8, "total_steps": 39, "loss": 1.0683, "lr": 4.498606908508754e-05, "epoch": 0.2006269592476489, "percentage": 20.51, "elapsed_time": "0:22:29", "remaining_time": "1:27:07", "throughput": 9326.64, "total_tokens": 12582912}
+{"current_steps": 9, "total_steps": 39, "loss": 1.0934, "lr": 4.371276870427753e-05, "epoch": 0.22570532915360503, "percentage": 23.08, "elapsed_time": "0:25:17", "remaining_time": "1:24:16", "throughput": 9331.22, "total_tokens": 14155776}
+{"current_steps": 10, "total_steps": 39, "loss": 1.0793, "lr": 4.231810883773999e-05, "epoch": 0.2507836990595611, "percentage": 25.64, "elapsed_time": "0:28:04", "remaining_time": "1:21:25", "throughput": 9335.8, "total_tokens": 15728640}
+{"current_steps": 11, "total_steps": 39, "loss": 1.049, "lr": 4.0811134389884433e-05, "epoch": 0.27586206896551724, "percentage": 28.21, "elapsed_time": "0:30:53", "remaining_time": "1:18:37", "throughput": 9334.55, "total_tokens": 17301504}
+{"current_steps": 12, "total_steps": 39, "loss": 1.0616, "lr": 3.920161866827889e-05, "epoch": 0.30094043887147337, "percentage": 30.77, "elapsed_time": "0:33:41", "remaining_time": "1:15:49", "throughput": 9334.57, "total_tokens": 18874368}
+{"current_steps": 13, "total_steps": 39, "loss": 1.0608, "lr": 3.7500000000000003e-05, "epoch": 0.32601880877742945, "percentage": 33.33, "elapsed_time": "0:36:29", "remaining_time": "1:12:59", "throughput": 9337.69, "total_tokens": 20447232}
+{"current_steps": 14, "total_steps": 39, "loss": 1.0509, "lr": 3.5717314035076355e-05, "epoch": 0.3510971786833856, "percentage": 35.9, "elapsed_time": "0:39:17", "remaining_time": "1:10:10", "throughput": 9339.57, "total_tokens": 22020096}
+{"current_steps": 15, "total_steps": 39, "loss": 1.0264, "lr": 3.386512217606339e-05, "epoch": 0.3761755485893417, "percentage": 38.46, "elapsed_time": "0:42:05", "remaining_time": "1:07:20", "throughput": 9341.52, "total_tokens": 23592960}
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+{"current_steps": 17, "total_steps": 39, "loss": 1.0238, "lr": 3.0000642344401113e-05, "epoch": 0.4263322884012539, "percentage": 43.59, "elapsed_time": "0:47:41", "remaining_time": "1:01:43", "throughput": 9343.32, "total_tokens": 26738688}
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+{"current_steps": 21, "total_steps": 39, "loss": 1.0343, "lr": 2.1986582993616926e-05, "epoch": 0.5266457680250783, "percentage": 53.85, "elapsed_time": "0:58:54", "remaining_time": "0:50:29", "throughput": 9344.46, "total_tokens": 33030144}
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trainer_state.json

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+{
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