Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1,5 @@
+all_results.json
+logs.txt
+trainer_log.jsonl
+training_loss.png
+train_results.json

README.md

@@ -0,0 +1,59 @@
+---
+license: other
+base_model: internlm/internlm2-chat-7b
+tags:
+- llama-factory
+- generated_from_trainer
+model-index:
+- name: 9f100e26-d997-46e8-afee-721977a16ca9
+  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. -->
+
+# 9f100e26-d997-46e8-afee-721977a16ca9
+
+This model is a fine-tuned version of [/home/lirenhao/pretrained_models/internlm2-chat-7b/](https://huggingface.co//home/lirenhao/pretrained_models/internlm2-chat-7b/) on the cpsycoun 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: 1e-06
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 28
+- total_train_batch_size: 448
+- total_eval_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 9.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.37.1
+- Pytorch 2.1.2+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

all_results.json

@@ -0,0 +1,7 @@
+{
+    "epoch": 9.0,
+    "train_loss": 1.4981852107577853,
+    "train_runtime": 2910.9748,
+    "train_samples_per_second": 9.69,
+    "train_steps_per_second": 0.022
+}

config.json

@@ -0,0 +1,36 @@
+{
+  "_name_or_path": "/home/lirenhao/pretrained_models/internlm2-chat-7b/",
+  "architectures": [
+    "InternLM2ForCausalLM"
+  ],
+  "attn_implementation": "eager",
+  "auto_map": {
+    "AutoConfig": "configuration_internlm2.InternLM2Config",
+    "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
+    "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
+  },
+  "bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "internlm2",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "dynamic"
+  },
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.37.1",
+  "use_cache": false,
+  "vocab_size": 92544
+}

configuration_internlm2.py

@@ -0,0 +1,151 @@
+# coding=utf-8
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" InternLM2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        Example:
+
+    """
+    model_type = "internlm2"
+    _auto_class = "AutoConfig"
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation="eager",
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.bias = bias
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = "eager"
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.37.1"
+}

logs.txt

@@ -0,0 +1,981 @@
+[2024-02-01 14:20:07,768] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-02-01 14:20:09,368] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
+[2024-02-01 14:20:09,369] [INFO] [runner.py:568:main] cmd = /home/lirenhao/anaconda3/envs/llama_factory/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgM119 --master_addr=127.0.0.1 --master_port=2345 --enable_each_rank_log=None /home/lirenhao/projects/LLaMA-Factory/src/train_bash.py --deepspeed ds_config.json --stage sft --model_name_or_path /home/lirenhao/pretrained_models/internlm2-chat-7b/ --do_train --dataset cpsycoun --template intern2 --finetuning_type full --lora_target wqkv --output_dir /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9 --overwrite_cache --overwrite_output_dir --per_device_train_batch_size 4 --gradient_accumulation_steps 28 --lr_scheduler_type cosine --logging_steps 10 --save_steps 21 --learning_rate 1e-6 --num_train_epochs 9.0 --plot_loss --fp16
+[2024-02-01 14:20:12,819] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-02-01 14:20:14,435] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3]}
+[2024-02-01 14:20:14,436] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=4, node_rank=0
+[2024-02-01 14:20:14,436] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1, 2, 3]})
+[2024-02-01 14:20:14,436] [INFO] [launch.py:163:main] dist_world_size=4
+[2024-02-01 14:20:14,436] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3
+[2024-02-01 14:20:19,797] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-02-01 14:20:20,069] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-02-01 14:20:20,128] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-02-01 14:20:20,157] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2024-02-01 14:20:22,839] [INFO] [comm.py:637:init_distributed] cdb=None
+[2024-02-01 14:20:23,347] [INFO] [comm.py:637:init_distributed] cdb=None
+[2024-02-01 14:20:23,364] [INFO] [comm.py:637:init_distributed] cdb=None
+[2024-02-01 14:20:23,375] [INFO] [comm.py:637:init_distributed] cdb=None
+[2024-02-01 14:20:23,376] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Process rank: 2, device: cuda:2, n_gpu: 1
+  distributed training: True, compute dtype: torch.float16
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
+_n_gpu=1,
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_persistent_workers=False,
+dataloader_pin_memory=True,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=ds_config.json,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=False,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=None,
+evaluation_strategy=no,
+fp16=True,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+generation_config=None,
+generation_max_length=None,
+generation_num_beams=None,
+gradient_accumulation_steps=28,
+gradient_checkpointing=False,
+gradient_checkpointing_kwargs=None,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_num_input_tokens_seen=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=1e-06,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=2,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/runs/Feb01_14-20-22_siat-a100-4-02,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=10,
+logging_strategy=steps,
+lr_scheduler_kwargs={},
+lr_scheduler_type=cosine,
+max_grad_norm=1.0,
+max_steps=-1,
+metric_for_best_model=None,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=9.0,
+optim=adamw_torch,
+optim_args=None,
+output_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=8,
+per_device_train_batch_size=4,
+predict_with_generate=False,
+prediction_loss_only=False,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=[],
+resume_from_checkpoint=None,
+run_name=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=21,
+save_strategy=steps,
+save_total_limit=None,
+seed=42,
+skip_memory_metrics=True,
+sortish_sampler=False,
+split_batches=False,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=0,
+weight_decay=0.0,
+)
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1
+  distributed training: True, compute dtype: torch.float16
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
+_n_gpu=1,
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_persistent_workers=False,
+dataloader_pin_memory=True,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=ds_config.json,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=False,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=None,
+evaluation_strategy=no,
+fp16=True,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+generation_config=None,
+generation_max_length=None,
+generation_num_beams=None,
+gradient_accumulation_steps=28,
+gradient_checkpointing=False,
+gradient_checkpointing_kwargs=None,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_num_input_tokens_seen=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=1e-06,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=0,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/runs/Feb01_14-20-23_siat-a100-4-02,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=10,
+logging_strategy=steps,
+lr_scheduler_kwargs={},
+lr_scheduler_type=cosine,
+max_grad_norm=1.0,
+max_steps=-1,
+metric_for_best_model=None,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=9.0,
+optim=adamw_torch,
+optim_args=None,
+output_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=8,
+per_device_train_batch_size=4,
+predict_with_generate=False,
+prediction_loss_only=False,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=[],
+resume_from_checkpoint=None,
+run_name=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=21,
+save_strategy=steps,
+save_total_limit=None,
+seed=42,
+skip_memory_metrics=True,
+sortish_sampler=False,
+split_batches=False,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=0,
+weight_decay=0.0,
+)
+[INFO|tokenization_utils_base.py:2025] 2024-02-01 14:20:24,513 >> loading file ./tokenizer.model
+[INFO|tokenization_utils_base.py:2025] 2024-02-01 14:20:24,513 >> loading file added_tokens.json
+[INFO|tokenization_utils_base.py:2025] 2024-02-01 14:20:24,513 >> loading file special_tokens_map.json
+[INFO|tokenization_utils_base.py:2025] 2024-02-01 14:20:24,513 >> loading file tokenizer_config.json
+[INFO|tokenization_utils_base.py:2025] 2024-02-01 14:20:24,513 >> loading file tokenizer.json
+[INFO|configuration_utils.py:727] 2024-02-01 14:20:24,850 >> loading configuration file /home/lirenhao/pretrained_models/internlm2-chat-7b/config.json
+[INFO|configuration_utils.py:727] 2024-02-01 14:20:24,852 >> loading configuration file /home/lirenhao/pretrained_models/internlm2-chat-7b/config.json
+[INFO|configuration_utils.py:792] 2024-02-01 14:20:24,854 >> Model config InternLM2Config {
+  "_name_or_path": "/home/lirenhao/pretrained_models/internlm2-chat-7b/",
+  "architectures": [
+    "InternLM2ForCausalLM"
+  ],
+  "attn_implementation": "eager",
+  "auto_map": {
+    "AutoConfig": "configuration_internlm2.InternLM2Config",
+    "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
+    "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
+  },
+  "bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "internlm2",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 2.0,
+    "type": "dynamic"
+  },
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.37.1",
+  "use_cache": true,
+  "vocab_size": 92544
+}
+
+[INFO|modeling_utils.py:3475] 2024-02-01 14:20:24,903 >> loading weights file /home/lirenhao/pretrained_models/internlm2-chat-7b/pytorch_model.bin.index.json
+[INFO|modeling_utils.py:1428] 2024-02-01 14:20:24,903 >> Instantiating InternLM2ForCausalLM model under default dtype torch.float16.
+[INFO|configuration_utils.py:826] 2024-02-01 14:20:24,905 >> Generate config GenerationConfig {
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2
+}
+
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Process rank: 1, device: cuda:1, n_gpu: 1
+  distributed training: True, compute dtype: torch.float16
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
+_n_gpu=1,
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_persistent_workers=False,
+dataloader_pin_memory=True,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=ds_config.json,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=False,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=None,
+evaluation_strategy=no,
+fp16=True,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+generation_config=None,
+generation_max_length=None,
+generation_num_beams=None,
+gradient_accumulation_steps=28,
+gradient_checkpointing=False,
+gradient_checkpointing_kwargs=None,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_num_input_tokens_seen=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=1e-06,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=1,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/runs/Feb01_14-20-23_siat-a100-4-02,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=10,
+logging_strategy=steps,
+lr_scheduler_kwargs={},
+lr_scheduler_type=cosine,
+max_grad_norm=1.0,
+max_steps=-1,
+metric_for_best_model=None,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=9.0,
+optim=adamw_torch,
+optim_args=None,
+output_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=8,
+per_device_train_batch_size=4,
+predict_with_generate=False,
+prediction_loss_only=False,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=[],
+resume_from_checkpoint=None,
+run_name=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=21,
+save_strategy=steps,
+save_total_limit=None,
+seed=42,
+skip_memory_metrics=True,
+sortish_sampler=False,
+split_batches=False,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=0,
+weight_decay=0.0,
+)
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Process rank: 3, device: cuda:3, n_gpu: 1
+  distributed training: True, compute dtype: torch.float16
+02/01/2024 14:20:24 - INFO - llmtuner.hparams.parser - Training/evaluation parameters Seq2SeqTrainingArguments(
+_n_gpu=1,
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_persistent_workers=False,
+dataloader_pin_memory=True,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=ds_config.json,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=False,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_steps=None,
+evaluation_strategy=no,
+fp16=True,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+generation_config=None,
+generation_max_length=None,
+generation_num_beams=None,
+gradient_accumulation_steps=28,
+gradient_checkpointing=False,
+gradient_checkpointing_kwargs=None,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_num_input_tokens_seen=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=1e-06,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=3,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/runs/Feb01_14-20-23_siat-a100-4-02,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=10,
+logging_strategy=steps,
+lr_scheduler_kwargs={},
+lr_scheduler_type=cosine,
+max_grad_norm=1.0,
+max_steps=-1,
+metric_for_best_model=None,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=9.0,
+optim=adamw_torch,
+optim_args=None,
+output_dir=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=8,
+per_device_train_batch_size=4,
+predict_with_generate=False,
+prediction_loss_only=False,
+push_to_hub=False,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=[],
+resume_from_checkpoint=None,
+run_name=/home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=21,
+save_strategy=steps,
+save_total_limit=None,
+seed=42,
+skip_memory_metrics=True,
+sortish_sampler=False,
+split_batches=False,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=0,
+weight_decay=0.0,
+)
+
+  return self.fget.__get__(instance, owner)()
+
+  return self.fget.__get__(instance, owner)()
+
+  return self.fget.__get__(instance, owner)()
+
+  return self.fget.__get__(instance, owner)()
+
+02/01/2024 14:20:35 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
+02/01/2024 14:20:35 - INFO - llmtuner.model.adapter - Fine-tuning method: Full
+
+02/01/2024 14:20:36 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
+02/01/2024 14:20:36 - INFO - llmtuner.model.adapter - Fine-tuning method: Full
+
+[INFO|modeling_utils.py:4352] 2024-02-01 14:20:36,242 >> All model checkpoint weights were used when initializing InternLM2ForCausalLM.
+
+[INFO|modeling_utils.py:4360] 2024-02-01 14:20:36,242 >> All the weights of InternLM2ForCausalLM were initialized from the model checkpoint at /home/lirenhao/pretrained_models/internlm2-chat-7b/.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use InternLM2ForCausalLM for predictions without further training.
+[INFO|configuration_utils.py:779] 2024-02-01 14:20:36,247 >> loading configuration file /home/lirenhao/pretrained_models/internlm2-chat-7b/generation_config.json
+[INFO|configuration_utils.py:826] 2024-02-01 14:20:36,248 >> Generate config GenerationConfig {
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2
+}
+
+02/01/2024 14:20:36 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
+02/01/2024 14:20:36 - INFO - llmtuner.model.adapter - Fine-tuning method: Full
+
+02/01/2024 14:20:36 - INFO - llmtuner.model.patcher - Gradient checkpointing enabled.
+02/01/2024 14:20:36 - INFO - llmtuner.model.adapter - Fine-tuning method: Full
+02/01/2024 14:20:47 - INFO - llmtuner.model.loader - trainable params: 7737708544 || all params: 7737708544 || trainable%: 100.0000
+02/01/2024 14:20:48 - INFO - llmtuner.data.template - Add <|im_end|> to stop words.
+02/01/2024 14:20:48 - INFO - llmtuner.model.loader - trainable params: 7737708544 || all params: 7737708544 || trainable%: 100.0000
+02/01/2024 14:20:48 - INFO - llmtuner.model.loader - trainable params: 7737708544 || all params: 7737708544 || trainable%: 100.0000
+02/01/2024 14:20:49 - INFO - llmtuner.data.template - Add <|im_end|> to stop words.
+02/01/2024 14:20:49 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
+02/01/2024 14:20:49 - INFO - llmtuner.data.template - Add <|im_end|> to stop words.
+02/01/2024 14:20:49 - INFO - llmtuner.model.loader - trainable params: 7737708544 || all params: 7737708544 || trainable%: 100.0000
+02/01/2024 14:20:49 - INFO - llmtuner.data.template - Add <|im_end|> to stop words.
+Using custom data configuration default-7bf826ddf73c2f44
+Loading Dataset Infos from /home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/datasets/packaged_modules/json
+Overwrite dataset info from restored data version if exists.
+Loading Dataset info from /home/lirenhao/.cache/huggingface/datasets/json/default-7bf826ddf73c2f44/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96
+Found cached dataset json (/home/lirenhao/.cache/huggingface/datasets/json/default-7bf826ddf73c2f44/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)
+Loading Dataset info from /home/lirenhao/.cache/huggingface/datasets/json/default-7bf826ddf73c2f44/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96
+
+
+02/01/2024 14:20:53 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
+02/01/2024 14:20:53 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
+02/01/2024 14:20:53 - WARNING - llmtuner.data.utils - Checksum failed: missing SHA-1 hash value in dataset_info.json.
+
+
+
+Caching processed dataset at /home/lirenhao/.cache/huggingface/datasets/json/default-7bf826ddf73c2f44/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-7cecb244118aac13.arrow
+
+input_ids:
+[1, 92543, 9081, 364, 2770, 657, 589, 15358, 17993, 6843, 963, 505, 4576, 11146, 451, 60628, 60384, 60721, 62442, 60752, 4452, 285, 4576, 11146, 451, 60628, 60384, 60721, 62442, 60752, 313, 505, 395, 7659, 1813, 4287, 1762, 560, 505, 8020, 684, 36956, 15358, 31288, 451, 68589, 76659, 71581, 699, 1226, 505, 6342, 442, 517, 11100, 328, 10894, 328, 454, 51978, 756, 285, 4576, 11146, 451, 60628, 60384, 60721, 62442, 60752, 313, 777, 3696, 454, 19187, 19829, 4563, 435, 410, 4287, 12032, 684, 410, 1341, 1893, 569, 6519, 454, 262, 69093, 281, 92542, 364, 92543, 1008, 364, 85064, 60703, 60353, 68856, 68306, 61860, 62703, 69516, 68765, 68984, 60362, 60353, 60376, 68678, 60427, 69944, 60355, 92542, 364, 92543, 525, 11353, 364, 73406, 68865, 68364, 69377, 60353, 86839, 70004, 68364, 69516, 69461, 71677, 68287, 60353, 69029, 68831, 68287, 60355, 68931, 69702, 75326, 71838, 60403, 61860, 62703, 77797, 68540, 60355, 364, 92543, 1008, 364, 74820, 68399, 69088, 60677, 68540, 60353, 61032, 71155, 69059, 60355, 92542, 364, 92543, 525, 11353, 364, 91781, 60353, 73161, 80540, 60415, 82098, 60355, 72010, 71404, 60353, 60403, 76153, 68912, 60381, 74112, 61076, 60504, 364, 92543, 1008, 364, 68856, 68306, 68912, 68326, 75848, 68595, 60353, 69972, 71645, 68473, 68585, 60353, 60404, 68965, 61716, 60418, 68273, 60353, 70124, 70698, 60363, 60355, 68389, 60363, 69667, 68306, 68303, 60353, 61214, 68310, 68758, 68261, 70623, 60355, 92542, 364, 92543, 525, 11353, 364, 72010, 69030, 71711, 61076, 60504, 68522, 60353, 86004, 71645, 68629, 68804, 68592, 69095, 60504, 364, 92543, 1008, 364, 84386, 68268, 68315, 75835, 79506, 60353, 60404, 68965, 72245, 68306, 69377, 60355, 60363, 69836, 60427, 70681, 60353, 69281, 91457, 71102, 62600, 62792, 60425, 60355, 92542, 364, 92543, 525, 11353, 364, 82967, 69068, 87160, 68261, 60504, 68319, 60353, 60403, 70868, 69962, 60871, 69893, 60366, 73603, 68261, 60504, 364, 92543, 1008, 364, 68678, 70219, 92396, 84863, 73603, 68252, 71869, 76758, 60353, 68252, 78650, 68306, 71645, 60355, 60363, 70802, 68626, 71010, 73382, 69893, 60353, 60499, 69361, 61032, 68678, 72415, 60355, 92542, 364, 92543, 525, 11353, 364, 72010, 82120, 68381, 72415, 70860, 69209, 61076, 60504, 68522, 60353, 73880, 60359, 75493, 60359, 72415, 60504, 364, 92543, 1008, 364, 74212, 60353, 60363, 73408, 69836, 73880, 60381, 72415, 60355, 69097, 60353, 88720, 60382, 71343, 68254, 70861, 68892, 60459, 71356, 60586, 60355, 92542, 364, 92543, 525, 11353, 364, 68374, 69209, 73175, 68364, 77514, 61076, 60504, 68522, 60353, 68364, 70033, 60359, 69441, 60359, 68273, 60504, 364, 92543, 1008, 364, 88554, 60355, 60363, 68848, 70033, 81269, 60353, 68965, 60520, 80959, 60355, 68389, 60363, 79837, 80665, 60353, 70465, 70802, 70133, 60355, 92542, 364, 92543, 525, 11353, 364, 76273, 68374, 60353, 69060, 71958, 60353, 68364, 69516, 70848, 69715, 60354, 60355, 81425, 68831, 68637, 60353, 80698, 74131, 73382, 79309, 60355, 364, 92543, 1008, 364, 68369, 61076, 60504, 60462, 69735, 91900, 60827, 60504, 92542, 364, 92543, 525, 11353, 364, 68400, 60353, 69897, 68505, 68364, 69209, 60353, 68908, 69116, 60381, 82567, 69290, 60355, 68265, 60353, 73161, 72826, 68288, 69418, 68304, 68747, 60353, 69068, 60381, 71645, 60359, 68303, 82409, 68615, 69715, 60355, 364, 92543, 1008, 364, 82700, 69460, 70417, 60355, 86492, 60577, 69353, 68301, 60827, 60504, 92542, 364, 92543, 525, 11353, 364, 60577, 68505, 68364, 69209, 68301, 60355, 75630, 82261, 68300, 60353, 68848, 68427, 69836, 73880, 68319, 72415, 68269, 60353, 69095, 68540, 74465, 60504, 2]
+inputs:
+ <s> <|im_start|>  system
+You are an AI assistant whose name is InternLM (书生·浦语).
+- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
+- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.<|im_end|>
+ <|im_start|>  user
+心理咨询师，我觉得我的胸闷症状越来越严重了，这让我很害怕。<|im_end|>
+ <|im_start|> assistant
+我能理解你的感受，首先我们要明确你的症状并不是生理问题，而是心理问题。我们可以尝试找出引发你胸闷的心理原因。
+ <|im_start|>  user
+可是我一直都在找原因，却找不到答案。<|im_end|>
+ <|im_start|> assistant
+不要着急，我们会一步一步地解决这个问题。你能告诉我，你生活中的压力和困扰吗？
+ <|im_start|>  user
+我觉得我的压力主要来自于家庭，我和丈夫关系不好，他总是忙于工作，很少关心我。而且我担心我的孩子，怕他们出了什么意外。<|im_end|>
+ <|im_start|> assistant
+你能详细说说吗？比如，你和丈夫之间的问题具体是什么？
+ <|im_start|>  user
+我们经常因为一些小事争吵，他总是忽略我的感受。我感到很孤独，就像被困在一个牢笼里。<|im_end|>
+ <|im_start|> assistant
+这种感觉让你想起了什么？或者，你觉得自己在这段婚姻中失去了什么？
+ <|im_start|>  user
+让我想想……我觉得我失去了一个温馨的家，一个关��我的丈夫。我一直在努力维持这段婚姻，但现实却让我失望。<|im_end|>
+ <|im_start|> assistant
+你能体会到这种失望带来的情绪吗？比如，伤心、愤怒、失望？
+ <|im_start|>  user
+是的，我经常会感到伤心和失望。有时候，我甚至会怀疑自己的人生是不是选错了路。<|im_end|>
+ <|im_start|> assistant
+这些情绪会影响你的日常生活吗？比如，你的睡眠、饮食、工作？
+ <|im_start|>  user
+肯定的。我最近睡眠很差，总是做噩梦。而且我吃得也不好，体重一直在下降。<|im_end|>
+ <|im_start|> assistant
+了解到这些，我想告诉你，你的症状是可以改善的。我们可以通过心理治疗，帮助你走出这段困境。
+ <|im_start|>  user
+真的吗？那我要如何做呢？<|im_end|>
+ <|im_start|> assistant
+首先，我们要了解你的情绪，学会面对和接纳它们。然后，我们会教你怎么表达自己的需求，让你和丈夫、孩子之间的关系得到改善。
+ <|im_start|>  user
+听起来很有道理。那我们从哪里开始呢？<|im_end|>
+ <|im_start|>  assistant
+从了解你的情绪开始。试着回想一下，最近一次感到伤心或者失望的时候，是什么原因导致的？</s>
+label_ids:
+[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 73406, 68865, 68364, 69377, 60353, 86839, 70004, 68364, 69516, 69461, 71677, 68287, 60353, 69029, 68831, 68287, 60355, 68931, 69702, 75326, 71838, 60403, 61860, 62703, 77797, 68540, 60355, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 91781, 60353, 73161, 80540, 60415, 82098, 60355, 72010, 71404, 60353, 60403, 76153, 68912, 60381, 74112, 61076, 60504, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 72010, 69030, 71711, 61076, 60504, 68522, 60353, 86004, 71645, 68629, 68804, 68592, 69095, 60504, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 82967, 69068, 87160, 68261, 60504, 68319, 60353, 60403, 70868, 69962, 60871, 69893, 60366, 73603, 68261, 60504, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 72010, 82120, 68381, 72415, 70860, 69209, 61076, 60504, 68522, 60353, 73880, 60359, 75493, 60359, 72415, 60504, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 68374, 69209, 73175, 68364, 77514, 61076, 60504, 68522, 60353, 68364, 70033, 60359, 69441, 60359, 68273, 60504, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 76273, 68374, 60353, 69060, 71958, 60353, 68364, 69516, 70848, 69715, 60354, 60355, 81425, 68831, 68637, 60353, 80698, 74131, 73382, 79309, 60355, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 68400, 60353, 69897, 68505, 68364, 69209, 60353, 68908, 69116, 60381, 82567, 69290, 60355, 68265, 60353, 73161, 72826, 68288, 69418, 68304, 68747, 60353, 69068, 60381, 71645, 60359, 68303, 82409, 68615, 69715, 60355, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 60577, 68505, 68364, 69209, 68301, 60355, 75630, 82261, 68300, 60353, 68848, 68427, 69836, 73880, 68319, 72415, 68269, 60353, 69095, 68540, 74465, 60504, 2]
+labels:
+ 我能理解你的感受，首先我们要明确你的症状并不是生理问题，而是心理问题。我们可以尝试找出引发你胸闷的心理原因。</s> 不要着急，我们会一步一步地解决这个问题。你能告诉我，你生活中的压力和困扰吗？</s> 你能详细说说吗？比如，你和丈夫之间的问题具体是什么？</s> 这种感觉让你想起了什么？或者，你觉得自己在这段婚姻中失去了��么？</s> 你能体会到这种失望带来的情绪吗？比如，伤心、愤怒、失望？</s> 这些情绪会影响你的日常生活吗？比如，你的睡眠、饮食、工作？</s> 了解到这些，我想告诉你，你的症状是可以改善的。我们可以通过心理治疗，帮助你走出这段困境。</s> 首先，我们要了解你的情绪，学会面对和接纳它们。然后，我们会教你怎么表达自己的需求，让你和丈夫、孩子之间的关系得到改善。</s> 从了解你的情绪开始。试着回想一下，最近一次感到伤心或者失望的时候，是什么原因导致的？</s>
+[INFO|training_args.py:1828] 2024-02-01 14:21:08,098 >> PyTorch: setting up devices
+
+  warnings.warn(
+Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[INFO|trainer.py:571] 2024-02-01 14:21:08,153 >> Using auto half precision backend
+[2024-02-01 14:21:08,351] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed info: version=0.13.1, git-hash=unknown, git-branch=unknown
+
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/transformers/training_args.py:1741: FutureWarning: `--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--hub_token` instead.
+  warnings.warn(
+
+
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/transformers/training_args.py:1741: FutureWarning: `--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--hub_token` instead.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/transformers/training_args.py:1741: FutureWarning: `--push_to_hub_token` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--hub_token` instead.
+  warnings.warn(
+[2024-02-01 14:21:41,776] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
+[2024-02-01 14:21:41,778] [INFO] [logging.py:96:log_dist] [Rank 0] Using client Optimizer as basic optimizer
+[2024-02-01 14:21:41,778] [INFO] [logging.py:96:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
+[2024-02-01 14:21:41,794] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Basic Optimizer = AdamW
+[2024-02-01 14:21:41,794] [INFO] [utils.py:56:is_zero_supported_optimizer] Checking ZeRO support for optimizer=AdamW type=<class 'torch.optim.adamw.AdamW'>
+[2024-02-01 14:21:41,794] [INFO] [logging.py:96:log_dist] [Rank 0] Creating torch.float16 ZeRO stage 2 optimizer
+[2024-02-01 14:21:41,795] [INFO] [stage_1_and_2.py:143:__init__] Reduce bucket size 500000000
+[2024-02-01 14:21:41,795] [INFO] [stage_1_and_2.py:144:__init__] Allgather bucket size 500000000
+[2024-02-01 14:21:41,795] [INFO] [stage_1_and_2.py:145:__init__] CPU Offload: False
+[2024-02-01 14:21:41,795] [INFO] [stage_1_and_2.py:146:__init__] Round robin gradient partitioning: False
+[2024-02-01 14:22:01,253] [INFO] [utils.py:791:see_memory_usage] Before initializing optimizer states
+[2024-02-01 14:22:01,254] [INFO] [utils.py:792:see_memory_usage] MA 22.12 GB         Max_MA 25.72 GB         CA 25.85 GB         Max_CA 26 GB 
+[2024-02-01 14:22:01,254] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 119.45 GB, percent = 12.4%
+[2024-02-01 14:22:01,614] [INFO] [utils.py:791:see_memory_usage] After initializing optimizer states
+[2024-02-01 14:22:01,615] [INFO] [utils.py:792:see_memory_usage] MA 36.53 GB         Max_MA 50.95 GB         CA 54.68 GB         Max_CA 55 GB 
+[2024-02-01 14:22:01,615] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 109.91 GB, percent = 11.4%
+[2024-02-01 14:22:01,615] [INFO] [stage_1_and_2.py:533:__init__] optimizer state initialized
+[2024-02-01 14:22:01,876] [INFO] [utils.py:791:see_memory_usage] After initializing ZeRO optimizer
+[2024-02-01 14:22:01,877] [INFO] [utils.py:792:see_memory_usage] MA 36.53 GB         Max_MA 36.53 GB         CA 54.68 GB         Max_CA 55 GB 
+[2024-02-01 14:22:01,878] [INFO] [utils.py:799:see_memory_usage] CPU Virtual Memory:  used = 101.72 GB, percent = 10.5%
+[2024-02-01 14:22:01,881] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Final Optimizer = AdamW
+[2024-02-01 14:22:01,881] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed using client LR scheduler
+[2024-02-01 14:22:01,881] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed LR Scheduler = None
+[2024-02-01 14:22:01,881] [INFO] [logging.py:96:log_dist] [Rank 0] step=0, skipped=0, lr=[1e-06], mom=[(0.9, 0.999)]
+[2024-02-01 14:22:01,883] [INFO] [config.py:984:print] DeepSpeedEngine configuration:
+[2024-02-01 14:22:01,883] [INFO] [config.py:988:print]   activation_checkpointing_config  {
+    "partition_activations": false, 
+    "contiguous_memory_optimization": false, 
+    "cpu_checkpointing": false, 
+    "number_checkpoints": null, 
+    "synchronize_checkpoint_boundary": false, 
+    "profile": false
+}
+[2024-02-01 14:22:01,883] [INFO] [config.py:988:print]   aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
+[2024-02-01 14:22:01,883] [INFO] [config.py:988:print]   amp_enabled .................. False
+[2024-02-01 14:22:01,883] [INFO] [config.py:988:print]   amp_params ................... False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   autotuning_config ............ {
+    "enabled": false, 
+    "start_step": null, 
+    "end_step": null, 
+    "metric_path": null, 
+    "arg_mappings": null, 
+    "metric": "throughput", 
+    "model_info": null, 
+    "results_dir": "autotuning_results", 
+    "exps_dir": "autotuning_exps", 
+    "overwrite": true, 
+    "fast": true, 
+    "start_profile_step": 3, 
+    "end_profile_step": 5, 
+    "tuner_type": "gridsearch", 
+    "tuner_early_stopping": 5, 
+    "tuner_num_trials": 50, 
+    "model_info_path": null, 
+    "mp_size": 1, 
+    "max_train_batch_size": null, 
+    "min_train_batch_size": 1, 
+    "max_train_micro_batch_size_per_gpu": 1.024000e+03, 
+    "min_train_micro_batch_size_per_gpu": 1, 
+    "num_tuning_micro_batch_sizes": 3
+}
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   bfloat16_enabled ............. False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   checkpoint_parallel_write_pipeline  False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   checkpoint_tag_validation_enabled  True
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   checkpoint_tag_validation_fail  False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7f7f6152d840>
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   communication_data_type ...... None
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   curriculum_enabled_legacy .... False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   curriculum_params_legacy ..... False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   data_efficiency_enabled ...... False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   dataloader_drop_last ......... False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   disable_allgather ............ False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   dump_state ................... False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   dynamic_loss_scale_args ...... {'init_scale': 65536, 'scale_window': 1000, 'delayed_shift': 2, 'consecutive_hysteresis': False, 'min_scale': 1}
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_enabled ........... False
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_gas_boundary_resolution  1
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_layer_name ........ bert.encoder.layer
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_layer_num ......... 0
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_max_iter .......... 100
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_stability ......... 1e-06
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_tol ............... 0.01
+[2024-02-01 14:22:01,884] [INFO] [config.py:988:print]   eigenvalue_verbose ........... False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   elasticity_enabled ........... False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   flops_profiler_config ........ {
+    "enabled": false, 
+    "recompute_fwd_factor": 0.0, 
+    "profile_step": 1, 
+    "module_depth": -1, 
+    "top_modules": 1, 
+    "detailed": true, 
+    "output_file": null
+}
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   fp16_auto_cast ............... False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   fp16_enabled ................. True
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   fp16_master_weights_and_gradients  False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   global_rank .................. 0
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   grad_accum_dtype ............. None
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   gradient_accumulation_steps .. 28
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   gradient_clipping ............ 1.0
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   gradient_predivide_factor .... 1.0
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   graph_harvesting ............. False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   initial_dynamic_scale ........ 65536
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   load_universal_checkpoint .... False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   loss_scale ................... 0
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   memory_breakdown ............. False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   mics_hierarchial_params_gather  False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   mics_shard_size .............. -1
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') enabled=False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   nebula_config ................ {
+    "enabled": false, 
+    "persistent_storage_path": null, 
+    "persistent_time_interval": 100, 
+    "num_of_version_in_retention": 2, 
+    "enable_nebula_load": true, 
+    "load_path": null
+}
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   optimizer_legacy_fusion ...... False
+[2024-02-01 14:22:01,885] [INFO] [config.py:988:print]   optimizer_name ............... None
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   optimizer_params ............. None
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   pld_enabled .................. False
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   pld_params ................... False
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   prescale_gradients ........... False
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   scheduler_name ............... None
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   scheduler_params ............. None
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   seq_parallel_communication_data_type  torch.float32
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   sparse_attention ............. None
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   sparse_gradients_enabled ..... False
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   steps_per_print .............. inf
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   train_batch_size ............. 448
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   train_micro_batch_size_per_gpu  4
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   use_data_before_expert_parallel_  False
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   use_node_local_storage ....... False
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   wall_clock_breakdown ......... False
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   weight_quantization_config ... None
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   world_size ................... 4
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   zero_allow_untested_optimizer  True
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   zero_config .................. stage=2 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500000000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500000000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=None offload_optimizer=None sub_group_size=1,000,000,000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=50,000,000 param_persistence_threshold=100,000 model_persistence_threshold=sys.maxsize max_live_parameters=1,000,000,000 max_reuse_distance=1,000,000,000 gather_16bit_weights_on_model_save=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True pipeline_loading_checkpoint=False override_module_apply=True
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   zero_enabled ................. True
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   zero_force_ds_cpu_optimizer .. True
+[2024-02-01 14:22:01,886] [INFO] [config.py:988:print]   zero_optimization_stage ...... 2
+[2024-02-01 14:22:01,887] [INFO] [config.py:974:print_user_config]   json = {
+    "train_batch_size": 448, 
+    "train_micro_batch_size_per_gpu": 4, 
+    "gradient_accumulation_steps": 28, 
+    "gradient_clipping": 1.0, 
+    "zero_allow_untested_optimizer": true, 
+    "fp16": {
+        "enabled": true, 
+        "loss_scale": 0, 
+        "initial_scale_power": 16, 
+        "loss_scale_window": 1000, 
+        "hysteresis": 2, 
+        "min_loss_scale": 1
+    }, 
+    "zero_optimization": {
+        "stage": 2, 
+        "allgather_partitions": true, 
+        "allgather_bucket_size": 5.000000e+08, 
+        "reduce_scatter": true, 
+        "reduce_bucket_size": 5.000000e+08, 
+        "overlap_comm": false, 
+        "contiguous_gradients": true
+    }, 
+    "steps_per_print": inf, 
+    "bf16": {
+        "enabled": false
+    }
+}
+[INFO|trainer.py:1721] 2024-02-01 14:22:01,887 >> ***** Running training *****
+[INFO|trainer.py:1722] 2024-02-01 14:22:01,887 >>   Num examples = 3,134
+[INFO|trainer.py:1723] 2024-02-01 14:22:01,887 >>   Num Epochs = 9
+[INFO|trainer.py:1724] 2024-02-01 14:22:01,887 >>   Instantaneous batch size per device = 4
+[INFO|trainer.py:1727] 2024-02-01 14:22:01,887 >>   Total train batch size (w. parallel, distributed & accumulation) = 448
+[INFO|trainer.py:1728] 2024-02-01 14:22:01,887 >>   Gradient Accumulation steps = 28
+[INFO|trainer.py:1729] 2024-02-01 14:22:01,887 >>   Total optimization steps = 63
+[INFO|trainer.py:1730] 2024-02-01 14:22:01,889 >>   Number of trainable parameters = 7,737,708,544
+
  0%|          | 0/63 [00:00<?, ?it/s]/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py:1968: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
+  overflow_gpu = get_accelerator().ByteTensor([overflow])
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py:1968: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
+  overflow_gpu = get_accelerator().ByteTensor([overflow])
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py:1968: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
+  overflow_gpu = get_accelerator().ByteTensor([overflow])
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/deepspeed/runtime/zero/stage_1_and_2.py:1968: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
+  overflow_gpu = get_accelerator().ByteTensor([overflow])
+
  2%|▏         | 1/63 [00:44<45:32, 44.06s/it]
  3%|▎         | 2/63 [01:23<42:01, 41.33s/it]
  5%|▍         | 3/63 [02:04<41:04, 41.08s/it]
  6%|▋         | 4/63 [02:43<39:37, 40.30s/it]
  8%|▊         | 5/63 [03:22<38:23, 39.72s/it][2024-02-01 14:26:04,941] [INFO] [loss_scaler.py:190:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 65536, but hysteresis is 2. Reducing hysteresis to 1
+
 10%|▉         | 6/63 [04:03<38:08, 40.15s/it][2024-02-01 14:26:44,502] [INFO] [loss_scaler.py:183:update_scale] [deepspeed] OVERFLOW! Rank 0 Skipping step. Attempted loss scale: 65536, reducing to 32768
+
 11%|█         | 7/63 [04:42<37:17, 39.96s/it]
 13%|█▎        | 8/63 [05:21<36:23, 39.71s/it]
 14%|█▍        | 9/63 [06:01<35:39, 39.62s/it]
 16%|█▌        | 10/63 [06:41<35:16, 39.93s/it]
                                               
+
 16%|█▌        | 10/63 [06:41<35:16, 39.93s/it]
 17%|█▋        | 11/63 [07:20<34:22, 39.65s/it]
 19%|█▉        | 12/63 [08:00<33:47, 39.76s/it]
 21%|██        | 13/63 [08:39<32:56, 39.53s/it]
 22%|██▏       | 14/63 [09:20<32:32, 39.85s/it]
 24%|██▍       | 15/63 [09:59<31:45, 39.69s/it]
 25%|██▌       | 16/63 [10:38<30:47, 39.31s/it]
 27%|██▋       | 17/63 [11:19<30:31, 39.82s/it]
 29%|██▊       | 18/63 [11:58<29:51, 39.81s/it]
 30%|███       | 19/63 [12:39<29:15, 39.89s/it]
 32%|███▏      | 20/63 [13:19<28:42, 40.06s/it]
                                               
+
 32%|███▏      | 20/63 [13:19<28:42, 40.06s/it]
 33%|███▎      | 21/63 [13:59<27:59, 39.99s/it][INFO|trainer.py:2926] 2024-02-01 14:36:12,897 >> Saving model checkpoint to /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21
+[INFO|configuration_utils.py:473] 2024-02-01 14:36:12,902 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/config.json
+[INFO|configuration_utils.py:594] 2024-02-01 14:36:12,903 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/generation_config.json
+[INFO|modeling_utils.py:2503] 2024-02-01 14:36:40,422 >> 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 /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/model.safetensors.index.json.
+[INFO|tokenization_utils_base.py:2433] 2024-02-01 14:36:40,424 >> tokenizer config file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2442] 2024-02-01 14:36:40,424 >> Special tokens file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/special_tokens_map.json
+[2024-02-01 14:36:41,670] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step21 is about to be saved!
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
+  warnings.warn(
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
+  warnings.warn(
+[2024-02-01 14:36:41,683] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/global_step21/mp_rank_00_model_states.pt
+[2024-02-01 14:36:41,684] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/global_step21/mp_rank_00_model_states.pt...
+[2024-02-01 14:37:17,058] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/global_step21/mp_rank_00_model_states.pt.
+[2024-02-01 14:37:17,061] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/global_step21/zero_pp_rank_0_mp_rank_00_optim_states.pt...
+[2024-02-01 14:38:15,362] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/global_step21/zero_pp_rank_0_mp_rank_00_optim_states.pt.
+[2024-02-01 14:38:15,363] [INFO] [engine.py:3477:_save_zero_checkpoint] zero checkpoint saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-21/global_step21/zero_pp_rank_0_mp_rank_00_optim_states.pt
+[2024-02-01 14:38:15,363] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step21 is ready now!
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+
 35%|███▍      | 22/63 [16:52<54:35, 79.88s/it]
 37%|███▋      | 23/63 [17:32<45:23, 68.08s/it]
 38%|███▊      | 24/63 [18:15<39:17, 60.44s/it]
 40%|███▉      | 25/63 [18:54<34:12, 54.02s/it]
 41%|████▏     | 26/63 [19:33<30:33, 49.54s/it]
 43%|████▎     | 27/63 [20:12<27:49, 46.38s/it]
 44%|████▍     | 28/63 [20:51<25:45, 44.17s/it]
 46%|████▌     | 29/63 [21:31<24:19, 42.92s/it]
 48%|████▊     | 30/63 [22:11<23:07, 42.06s/it]
                                               
+
 48%|████▊     | 30/63 [22:11<23:07, 42.06s/it]
 49%|████▉     | 31/63 [22:52<22:17, 41.80s/it]
 51%|█████     | 32/63 [23:32<21:11, 41.02s/it]
 52%|█████▏    | 33/63 [24:10<20:06, 40.20s/it]
 54%|█████▍    | 34/63 [24:49<19:18, 39.96s/it]
 56%|█████▌    | 35/63 [25:30<18:43, 40.11s/it]
 57%|█████▋    | 36/63 [26:10<18:03, 40.13s/it]
 59%|█████▊    | 37/63 [26:49<17:12, 39.70s/it]
 60%|██████    | 38/63 [27:29<16:36, 39.88s/it]
 62%|██████▏   | 39/63 [28:08<15:48, 39.51s/it]
 63%|██████▎   | 40/63 [28:46<15:04, 39.34s/it]
                                               
+
 63%|██████▎   | 40/63 [28:46<15:04, 39.34s/it]
 65%|██████▌   | 41/63 [29:27<14:36, 39.84s/it]
 67%|██████▋   | 42/63 [30:07<13:57, 39.87s/it][INFO|trainer.py:2926] 2024-02-01 14:52:21,426 >> Saving model checkpoint to /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42
+[INFO|configuration_utils.py:473] 2024-02-01 14:52:21,431 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/config.json
+[INFO|configuration_utils.py:594] 2024-02-01 14:52:21,432 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/generation_config.json
+[INFO|modeling_utils.py:2503] 2024-02-01 14:52:48,702 >> 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 /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/model.safetensors.index.json.
+[INFO|tokenization_utils_base.py:2433] 2024-02-01 14:52:48,704 >> tokenizer config file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2442] 2024-02-01 14:52:48,704 >> Special tokens file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/special_tokens_map.json
+[2024-02-01 14:52:49,843] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step42 is about to be saved!
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
+  warnings.warn(
+[2024-02-01 14:52:49,856] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/global_step42/mp_rank_00_model_states.pt
+[2024-02-01 14:52:49,856] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/global_step42/mp_rank_00_model_states.pt...
+[2024-02-01 14:53:25,041] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/global_step42/mp_rank_00_model_states.pt.
+[2024-02-01 14:53:25,044] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/global_step42/zero_pp_rank_0_mp_rank_00_optim_states.pt...
+[2024-02-01 14:54:24,364] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/global_step42/zero_pp_rank_0_mp_rank_00_optim_states.pt.
+[2024-02-01 14:54:24,364] [INFO] [engine.py:3477:_save_zero_checkpoint] zero checkpoint saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-42/global_step42/zero_pp_rank_0_mp_rank_00_optim_states.pt
+[2024-02-01 14:54:24,364] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step42 is ready now!
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
+  warnings.warn(
+
 68%|██████▊   | 43/63 [33:01<26:37, 79.86s/it]
 70%|██████▉   | 44/63 [33:41<21:31, 67.96s/it]
 71%|███████▏  | 45/63 [34:20<17:47, 59.29s/it]
 73%|███████▎  | 46/63 [35:01<15:13, 53.75s/it]
 75%|███████▍  | 47/63 [35:41<13:13, 49.58s/it]
 76%|███████▌  | 48/63 [36:21<11:40, 46.71s/it]
 78%|███████▊  | 49/63 [37:00<10:25, 44.69s/it]
 79%|███████▉  | 50/63 [37:42<09:26, 43.60s/it]
                                               
+
 79%|███████▉  | 50/63 [37:42<09:26, 43.60s/it]
 81%|████████  | 51/63 [38:20<08:24, 42.07s/it]
 83%|████████▎ | 52/63 [39:00<07:34, 41.29s/it]
 84%|████████▍ | 53/63 [39:41<06:52, 41.22s/it]
 86%|████████▌ | 54/63 [40:21<06:07, 40.87s/it]
 87%|████████▋ | 55/63 [41:00<05:22, 40.28s/it]
 89%|████████▉ | 56/63 [41:38<04:39, 39.88s/it]
 90%|█████████ | 57/63 [42:18<03:58, 39.78s/it]
 92%|█████████▏| 58/63 [42:59<03:20, 40.10s/it]
 94%|█████████▎| 59/63 [43:39<02:40, 40.24s/it]
 95%|█████████▌| 60/63 [44:19<02:00, 40.04s/it]
                                               
+
 95%|█████████▌| 60/63 [44:19<02:00, 40.04s/it]
 97%|█████████▋| 61/63 [44:58<01:19, 39.67s/it]
 98%|█████████▊| 62/63 [45:38<00:39, 39.80s/it]
+[INFO|configuration_utils.py:473] 2024-02-01 15:08:30,328 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/config.json
+[INFO|configuration_utils.py:594] 2024-02-01 15:08:30,329 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/generation_config.json
+[INFO|modeling_utils.py:2503] 2024-02-01 15:08:57,391 >> 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 /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/model.safetensors.index.json.
+[INFO|tokenization_utils_base.py:2433] 2024-02-01 15:08:57,393 >> tokenizer config file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2442] 2024-02-01 15:08:57,393 >> Special tokens file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/special_tokens_map.json
+[2024-02-01 15:08:58,595] [INFO] [logging.py:96:log_dist] [Rank 0] [Torch] Checkpoint global_step63 is about to be saved!
+/home/lirenhao/anaconda3/envs/llama_factory/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
+  warnings.warn(
+[2024-02-01 15:08:58,608] [INFO] [logging.py:96:log_dist] [Rank 0] Saving model checkpoint: /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/global_step63/mp_rank_00_model_states.pt
+[2024-02-01 15:08:58,608] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/global_step63/mp_rank_00_model_states.pt...
+[2024-02-01 15:09:33,948] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/global_step63/mp_rank_00_model_states.pt.
+[2024-02-01 15:09:33,951] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/global_step63/zero_pp_rank_0_mp_rank_00_optim_states.pt...
+[2024-02-01 15:10:31,865] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/global_step63/zero_pp_rank_0_mp_rank_00_optim_states.pt.
+[2024-02-01 15:10:31,866] [INFO] [engine.py:3477:_save_zero_checkpoint] zero checkpoint saved /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tmp-checkpoint-63/global_step63/zero_pp_rank_0_mp_rank_00_optim_states.pt
+[2024-02-01 15:10:31,866] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step63 is ready now!
+[INFO|trainer.py:1962] 2024-02-01 15:10:32,863 >> 
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
                                               
+
+[INFO|trainer.py:2926] 2024-02-01 15:10:44,639 >> Saving model checkpoint to /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9
+[INFO|configuration_utils.py:473] 2024-02-01 15:10:44,787 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/config.json
+[INFO|configuration_utils.py:594] 2024-02-01 15:10:44,788 >> Configuration saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/generation_config.json
+[2024-02-01 15:10:49,742] [INFO] [launch.py:347:main] Process 3771596 exits successfully.
+[2024-02-01 15:10:49,742] [INFO] [launch.py:347:main] Process 3771597 exits successfully.
+[2024-02-01 15:10:49,742] [INFO] [launch.py:347:main] Process 3771598 exits successfully.
+[INFO|modeling_utils.py:2503] 2024-02-01 15:11:12,707 >> 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 /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/model.safetensors.index.json.
+[INFO|tokenization_utils_base.py:2433] 2024-02-01 15:11:12,709 >> tokenizer config file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2442] 2024-02-01 15:11:12,709 >> Special tokens file saved in /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/special_tokens_map.json
+***** train metrics *****
+  epoch                    =        9.0
+  train_loss               =     1.4982
+  train_runtime            = 0:48:30.97
+  train_samples_per_second =       9.69
+  train_steps_per_second   =      0.022
+Figure saved: /home/lirenhao/projects/LLaMA-Factory/output/9f100e26-d997-46e8-afee-721977a16ca9/training_loss.png
+02/01/2024 15:11:14 - WARNING - llmtuner.extras.ploting - No metric eval_loss to plot.
+[INFO|modelcard.py:452] 2024-02-01 15:11:14,095 >> Dropping the following result as it does not have all the necessary fields:
+{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
+[2024-02-01 15:11:17,773] [INFO] [launch.py:347:main] Process 3771595 exits successfully.

model-00001-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14397212a196150de59782fb606e42dd9cac1940623625bfef4d77dafd0faa66
+size 4885470560

model-00002-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db2e13cdd738cfbb7eee70e1999e01746d3f84ff978ac7083fcce11f49859c4e
+size 4915913344

model-00003-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bc3d5d1a4c26135bdb747af7aecc19cf9507f239c48e515351d699d4d0188ad
+size 4915938224

model-00004-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8938692be133cc903cab9d3e5a45bfd1b010667455b23bbf654d4cb00eebc958
+size 758120576

model.safetensors.index.json

@@ -0,0 +1,234 @@
+{
+  "metadata": {
+    "total_size": 15475417088
+  },
+  "weight_map": {
+    "model.layers.0.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.0.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.1.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.10.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.10.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.11.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.12.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.13.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.14.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.15.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.16.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.17.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.18.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.19.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.2.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.2.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.20.attention.wo.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.attention.wqkv.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.20.feed_forward.w1.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.20.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.20.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.21.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.22.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.23.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.24.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.25.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.26.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.27.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.28.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.29.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.3.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.3.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.30.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.30.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.attention.wo.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.attention.wqkv.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.attention_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.feed_forward.w1.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.feed_forward.w2.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.feed_forward.w3.weight": "model-00003-of-00004.safetensors",
+    "model.layers.31.ffn_norm.weight": "model-00003-of-00004.safetensors",
+    "model.layers.4.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.4.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.5.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.6.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.7.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.attention_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.feed_forward.w2.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.feed_forward.w3.weight": "model-00001-of-00004.safetensors",
+    "model.layers.8.ffn_norm.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.attention.wo.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.attention.wqkv.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.attention_norm.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.feed_forward.w1.weight": "model-00001-of-00004.safetensors",
+    "model.layers.9.feed_forward.w2.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.feed_forward.w3.weight": "model-00002-of-00004.safetensors",
+    "model.layers.9.ffn_norm.weight": "model-00002-of-00004.safetensors",
+    "model.norm.weight": "model-00003-of-00004.safetensors",
+    "model.tok_embeddings.weight": "model-00001-of-00004.safetensors",
+    "output.weight": "model-00004-of-00004.safetensors"
+  }
+}

modeling_internlm2.py

@@ -0,0 +1,1391 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch InternLM2 model."""
+import math
+import queue
+import threading
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except:  # noqa # pylint: disable=bare-except
+    BaseStreamer = None
+
+from .configuration_internlm2 import InternLM2Config
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "InternLM2Config"
+
+flash_attn_func, flash_attn_varlen_func = None, None
+pad_input, index_first_axis, unpad_input = None, None, None
+def _import_flash_attn():
+    global flash_attn_func, flash_attn_varlen_func
+    global pad_input, index_first_axis, unpad_input
+    try:
+        from flash_attn import flash_attn_func as _flash_attn_func, flash_attn_varlen_func as _flash_attn_varlen_func
+        from flash_attn.bert_padding import pad_input as _pad_input, index_first_axis as _index_first_axis, unpad_input as _unpad_input
+        flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+        pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    except ImportError:
+        raise ImportError("flash_attn is not installed.")
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
+class InternLM2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        InternLM2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
+class InternLM2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
+    Credits to the Reddit users /u/bloc97 and /u/emozilla.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class InternLM2MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
+
+        return down_proj
+
+
+# Copied from transformers.model.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaAttention
+class InternLM2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.wqkv = nn.Linear(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.bias,
+        )
+
+        self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = InternLM2RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "dynamic":
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == "linear":
+                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
+        return self.rotary_emb
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
+                "Please make sure use `attention_mask` instead.`"
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            "b q (h gs d) -> b q h gs d",
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
+class InternLM2FlashAttention2(InternLM2Attention):
+    """
+    InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # InternLM2FlashAttention2 attention does not support output_attentions
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
+                "Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            "b q (h gs d) -> b q h gs d",
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len
+        )
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        # Contains at least one padding token in the sequence
+        causal = self.is_causal and query_length != 1
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q.to(torch.int64),
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+INTERNLM2_ATTENTION_CLASSES = {
+    "eager": InternLM2Attention,
+    "flash_attention_2": InternLM2FlashAttention2,
+}
+
+# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
+class InternLM2DecoderLayer(nn.Module):
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
+
+        self.feed_forward = InternLM2MLP(config)
+        self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
+                "Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.attention_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+InternLM2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`InternLM2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
+@add_start_docstrings(
+    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2PreTrainedModel(PreTrainedModel):
+    config_class = InternLM2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["InternLM2DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+InternLM2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
+            when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaModel
+@add_start_docstrings(
+    "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2Model(InternLM2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
+
+    Args:
+        config: InternLM2Config
+    """
+
+    _auto_class = "AutoModel"
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.tok_embeddings = value
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.attn_implementation == "flash_attention_2":
+            _import_flash_attn()
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.tok_embeddings(input_ids)
+
+        if self.config.attn_implementation == "flash_attention_2":
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+                )
+            attention_mask = self._prepare_decoder_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
+class InternLM2ForCausalLM(InternLM2PreTrainedModel):
+    _auto_class = "AutoModelForCausalLM"
+
+    _tied_weights_keys = ["output.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.output
+
+    def set_output_embeddings(self, new_embeddings):
+        self.output = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
+
+        >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.output(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            past_length = past_key_values[0][0].shape[2]
+
+            # Some generation methods already pass only the last input ID
+            if input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=""):
+        if tokenizer.add_bos_token:
+            prompt = ""
+        else:
+            prompt = tokenizer.bos_token
+        if meta_instruction:
+            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
+        for record in history:
+            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
+        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
+        return tokenizer([prompt], return_tensors="pt")
+
+    @torch.no_grad()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        streamer: Optional[BaseStreamer] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
+        "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
+        "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.",
+        **kwargs,
+    ):
+        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
+        inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
+        # also add end-of-assistant token in eos token id to avoid unnecessary generation
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
+        outputs = self.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
+        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        response = response.split("<|im_end|>")[0]
+        history = history + [(query, response)]
+        return response, history
+
+    @torch.no_grad()
+    def stream_chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        """
+        Return a generator in format: (response, history)
+        Eg.
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
+        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
+        """
+        if BaseStreamer is None:
+            raise ModuleNotFoundError(
+                "The version of `transformers` is too low. Please make sure "
+                "that you have installed `transformers>=4.28.0`."
+            )
+
+        response_queue = queue.Queue(maxsize=20)
+
+        class ChatStreamer(BaseStreamer):
+            def __init__(self, tokenizer) -> None:
+                super().__init__()
+                self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ""
+                self.cache = []
+                self.received_inputs = False
+                self.queue.put((self.response, history + [(self.query, self.response)]))
+
+            def put(self, value):
+                if len(value.shape) > 1 and value.shape[0] > 1:
+                    raise ValueError("ChatStreamer only supports batch size 1")
+                elif len(value.shape) > 1:
+                    value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
+                self.cache.extend(value.tolist())
+                token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
+                if token.strip() != "<|im_end|>":
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
+                    self.cache = []
+                else:
+                    self.end()
+
+            def end(self):
+                self.queue.put(None)
+
+        def stream_producer():
+            return self.chat(
+                tokenizer=tokenizer,
+                query=query,
+                streamer=ChatStreamer(tokenizer=tokenizer),
+                history=history,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                **kwargs,
+            )
+
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is None:
+                    return
+                yield res
+
+        return consumer()
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
+    as other causal models (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+                    logits.device
+                )
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

special_tokens_map.json

@@ -0,0 +1,39 @@
+{
+  "additional_special_tokens": [
+    {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_internlm2.py

@@ -0,0 +1,236 @@
+# coding=utf-8
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tokenization classes for InternLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+
+# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
+class InternLM2Tokenizer(PreTrainedTokenizer):
+    """
+    Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+    _auto_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="</s>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
+        return self._no_prefix_space_tokens
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
+
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return self.sp_model.eos_id()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return " " + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
+size 1477754

tokenizer_config.json

@@ -0,0 +1,95 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92538": {
+      "content": "<|plugin|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92539": {
+      "content": "<|interpreter|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92540": {
+      "content": "<|action_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92541": {
+      "content": "<|action_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92542": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "92543": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_end|>"
+  ],
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_internlm2.InternLM2Tokenizer",
+      null
+    ]
+  },
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "InternLM2Tokenizer",
+  "unk_token": "<unk>"
+}

train_results.json

@@ -0,0 +1,7 @@
+{
+    "epoch": 9.0,
+    "train_loss": 1.4981852107577853,
+    "train_runtime": 2910.9748,
+    "train_samples_per_second": 9.69,
+    "train_steps_per_second": 0.022
+}

trainer_log.jsonl

@@ -0,0 +1,7 @@
+{"current_steps": 10, "total_steps": 63, "loss": 2.1176, "eval_loss": null, "predict_loss": null, "reward": null, "learning_rate": 9.607381059352038e-07, "epoch": 1.43, "percentage": 15.87, "elapsed_time": "0:06:41", "remaining_time": "0:35:29"}
+{"current_steps": 20, "total_steps": 63, "loss": 1.5678, "eval_loss": null, "predict_loss": null, "reward": null, "learning_rate": 8.117449009293668e-07, "epoch": 2.86, "percentage": 31.75, "elapsed_time": "0:13:19", "remaining_time": "0:28:39"}
+{"current_steps": 30, "total_steps": 63, "loss": 1.4173, "eval_loss": null, "predict_loss": null, "reward": null, "learning_rate": 5.868240888334652e-07, "epoch": 4.29, "percentage": 47.62, "elapsed_time": "0:22:11", "remaining_time": "0:24:24"}
+{"current_steps": 40, "total_steps": 63, "loss": 1.342, "eval_loss": null, "predict_loss": null, "reward": null, "learning_rate": 3.4075667487415785e-07, "epoch": 5.71, "percentage": 63.49, "elapsed_time": "0:28:46", "remaining_time": "0:16:32"}
+{"current_steps": 50, "total_steps": 63, "loss": 1.3108, "eval_loss": null, "predict_loss": null, "reward": null, "learning_rate": 1.3347406408508694e-07, "epoch": 7.14, "percentage": 79.37, "elapsed_time": "0:37:42", "remaining_time": "0:09:48"}
+{"current_steps": 60, "total_steps": 63, "loss": 1.2987, "eval_loss": null, "predict_loss": null, "reward": null, "learning_rate": 1.5461356885461075e-08, "epoch": 8.57, "percentage": 95.24, "elapsed_time": "0:44:19", "remaining_time": "0:02:12"}
+{"current_steps": 63, "total_steps": 63, "loss": null, "eval_loss": null, "predict_loss": null, "reward": null, "learning_rate": null, "epoch": 9.0, "percentage": 100.0, "elapsed_time": "0:46:16", "remaining_time": "0:00:00"}

trainer_state.json

@@ -0,0 +1,66 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 9.0,
+  "eval_steps": 500,
+  "global_step": 63,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 1.43,
+      "learning_rate": 9.607381059352038e-07,
+      "loss": 2.1176,
+      "step": 10
+    },
+    {
+      "epoch": 2.86,
+      "learning_rate": 8.117449009293668e-07,
+      "loss": 1.5678,
+      "step": 20
+    },
+    {
+      "epoch": 4.29,
+      "learning_rate": 5.868240888334652e-07,
+      "loss": 1.4173,
+      "step": 30
+    },
+    {
+      "epoch": 5.71,
+      "learning_rate": 3.4075667487415785e-07,
+      "loss": 1.342,
+      "step": 40
+    },
+    {
+      "epoch": 7.14,
+      "learning_rate": 1.3347406408508694e-07,
+      "loss": 1.3108,
+      "step": 50
+    },
+    {
+      "epoch": 8.57,
+      "learning_rate": 1.5461356885461075e-08,
+      "loss": 1.2987,
+      "step": 60
+    },
+    {
+      "epoch": 9.0,
+      "step": 63,
+      "total_flos": 8.187554675294208e+17,
+      "train_loss": 1.4981852107577853,
+      "train_runtime": 2910.9748,
+      "train_samples_per_second": 9.69,
+      "train_steps_per_second": 0.022
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 63,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 9,
+  "save_steps": 21,
+  "total_flos": 8.187554675294208e+17,
+  "train_batch_size": 4,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:587330976961c8bd5af75d46b680e9a44df1210ab6567118a65fa36696393e87
+size 6200