Submitting job: /common/home/users/d/dh.huang.2023/code/logical-reasoning/scripts/tune-mgtv.sh
Current Directory:
/common/home/users/d/dh.huang.2023/code/logical-reasoning
Tue Jul 16 09:15:08 2024       
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|  No running processes found                                                             |
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Linux lexicon 4.18.0-553.5.1.el8_10.x86_64 #1 SMP Thu Jun 6 09:41:19 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
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Tuning shenzhi-wang/Llama3-8B-Chinese-Chat with config/llama3-8b_lora_sft_bf16-p1.yaml
Current Directory:
/common/home/users/d/dh.huang.2023/code/logical-reasoning/llama-factory
config/llama3-8b_lora_sft_bf16-p1.yaml:
 {
  "model_name_or_path": "shenzhi-wang/Llama3-8B-Chinese-Chat",
  "stage": "sft",
  "do_train": true,
  "finetuning_type": "lora",
  "lora_target": "all",
  "loraplus_lr_ratio": 16.0,
  "upcast_layernorm": true,
  "dataset": "alpaca_mgtv_p1",
  "template": "llama3",
  "cutoff_len": 4096,
  "max_samples": 25000,
  "overwrite_cache": true,
  "preprocessing_num_workers": 16,
  "output_dir": "saves/llama3-8b/lora/sft_bf16_p1_full",
  "logging_steps": 10,
  "save_steps": 175,
  "plot_loss": true,
  "overwrite_output_dir": true,
  "per_device_train_batch_size": 16,
  "gradient_accumulation_steps": 8,
  "learning_rate": 0.0001,
  "num_train_epochs": 6.0,
  "lr_scheduler_type": "cosine",
  "warmup_ratio": 0.1,
  "bf16": true,
  "ddp_timeout": 180000000,
  "val_size": 0.1,
  "per_device_eval_batch_size": 1,
  "eval_strategy": "steps",
  "eval_steps": 175,
  "report_to": "wandb",
  "run_name": "llama3_8b_p1_full"
}
07/16/2024 09:15:26 - INFO - llamafactory.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.bfloat16
[INFO|tokenization_utils_base.py:2108] 2024-07-16 09:15:26,639 >> loading file tokenizer.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/tokenizer.json
[INFO|tokenization_utils_base.py:2108] 2024-07-16 09:15:26,639 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2108] 2024-07-16 09:15:26,639 >> loading file special_tokens_map.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/special_tokens_map.json
[INFO|tokenization_utils_base.py:2108] 2024-07-16 09:15:26,639 >> loading file tokenizer_config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/tokenizer_config.json
[WARNING|logging.py:314] 2024-07-16 09:15:27,174 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
07/16/2024 09:15:27 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
07/16/2024 09:15:27 - INFO - llamafactory.data.loader - Loading dataset alpaca_mgtv_p1.json...

Converting format of dataset (num_proc=16):   0%|          | 0/25000 [00:00<?, ? examples/s]
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Converting format of dataset (num_proc=16): 100%|██████████| 25000/25000 [00:00<00:00, 108089.59 examples/s]

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Running tokenizer on dataset (num_proc=16):  96%|█████████▌| 23876/25000 [00:03<00:00, 13285.02 examples/s]
Running tokenizer on dataset (num_proc=16): 100%|██████████| 25000/25000 [00:03<00:00, 6701.81 examples/s] 
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 09:15:32,903 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 09:15:32,903 >> Model config LlamaConfig {
  "_name_or_path": "shenzhi-wang/Llama3-8B-Chinese-Chat",
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|modeling_utils.py:3474] 2024-07-16 09:15:33,043 >> loading weights file model.safetensors from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/model.safetensors.index.json
[INFO|modeling_utils.py:1519] 2024-07-16 09:15:33,051 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
[INFO|configuration_utils.py:962] 2024-07-16 09:15:33,052 >> Generate config GenerationConfig {
  "bos_token_id": 128000,
  "eos_token_id": 128009
}

input_ids:
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inputs:
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

你是一个逻辑游戏的主持人。游戏规则如下：

1. 参与者会得到一个谜题。
2. 参与者可以通过提问来获取线索，尝试解开谜题。
3. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。
4. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。
5. 参与者需要根据回答来推理，并最终找出谜题的正确答案。

请严格按照这些规则回答参与者提出的问题。

谜题: 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。

实际情况: 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。

参与者提出的问题: 偷的人信神吗
<|eot_id|><|start_header_id|>assistant<|end_header_id|>

不是<|eot_id|>
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, -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, -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, -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, 103668, 128009]
labels:
不是<|eot_id|>

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
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Loading checkpoint shards: 100%|██████████| 4/4 [00:17<00:00,  4.27s/it]
[INFO|modeling_utils.py:4280] 2024-07-16 09:15:50,224 >> All model checkpoint weights were used when initializing LlamaForCausalLM.

[INFO|modeling_utils.py:4288] 2024-07-16 09:15:50,224 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at shenzhi-wang/Llama3-8B-Chinese-Chat.
If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
[INFO|configuration_utils.py:917] 2024-07-16 09:15:50,463 >> loading configuration file generation_config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/generation_config.json
[INFO|configuration_utils.py:962] 2024-07-16 09:15:50,463 >> Generate config GenerationConfig {
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "pad_token_id": 128009
}

07/16/2024 09:15:50 - INFO - llamafactory.model.model_utils.checkpointing - Upcasting layernorm weights in float32.
07/16/2024 09:15:50 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
07/16/2024 09:15:50 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
07/16/2024 09:15:50 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
07/16/2024 09:15:50 - INFO - llamafactory.model.adapter - Fine-tuning method: LoRA
07/16/2024 09:15:50 - INFO - llamafactory.model.model_utils.misc - Found linear modules: down_proj,up_proj,gate_proj,k_proj,v_proj,o_proj,q_proj
07/16/2024 09:15:50 - INFO - llamafactory.model.loader - trainable params: 20,971,520 || all params: 8,051,232,768 || trainable%: 0.2605
Detected kernel version 4.18.0, 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:641] 2024-07-16 09:15:51,000 >> Using auto half precision backend
07/16/2024 09:15:51 - WARNING - llamafactory.train.callbacks - Previous trainer log in this folder will be deleted.
07/16/2024 09:15:51 - INFO - llamafactory.train.trainer_utils - Using LoRA+ optimizer with loraplus lr ratio 16.00.
[INFO|trainer.py:2078] 2024-07-16 09:15:51,222 >> ***** Running training *****
[INFO|trainer.py:2079] 2024-07-16 09:15:51,222 >>   Num examples = 22,500
[INFO|trainer.py:2080] 2024-07-16 09:15:51,222 >>   Num Epochs = 6
[INFO|trainer.py:2081] 2024-07-16 09:15:51,222 >>   Instantaneous batch size per device = 16
[INFO|trainer.py:2084] 2024-07-16 09:15:51,222 >>   Total train batch size (w. parallel, distributed & accumulation) = 128
[INFO|trainer.py:2085] 2024-07-16 09:15:51,222 >>   Gradient Accumulation steps = 8
[INFO|trainer.py:2086] 2024-07-16 09:15:51,222 >>   Total optimization steps = 1,050
[INFO|trainer.py:2087] 2024-07-16 09:15:51,225 >>   Number of trainable parameters = 20,971,520
[INFO|integration_utils.py:723] 2024-07-16 09:15:51,228 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
wandb: Currently logged in as: inflaton-sg (inflaton-ai). Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.17.4
wandb: Run data is saved locally in /common2/dh.huang.2023/code/logical-reasoning/llama-factory/wandb/run-20240716_091552-t1nqedbn
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run llama3_8b_p1_full
wandb: ⭐️ View project at https://wandb.ai/inflaton-ai/huggingface
wandb: 🚀 View run at https://wandb.ai/inflaton-ai/huggingface/runs/t1nqedbn

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[INFO|trainer.py:3721] 2024-07-16 11:19:02,509 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-16 11:19:02,509 >>   Batch size = 1
{'loss': 0.9166, 'grad_norm': 1.478421688079834, 'learning_rate': 9.523809523809523e-06, 'epoch': 0.06}
{'loss': 0.3687, 'grad_norm': 0.9236305356025696, 'learning_rate': 1.9047619047619046e-05, 'epoch': 0.11}
{'loss': 0.328, 'grad_norm': 0.6159961223602295, 'learning_rate': 2.857142857142857e-05, 'epoch': 0.17}
{'loss': 0.3049, 'grad_norm': 1.8057916164398193, 'learning_rate': 3.809523809523809e-05, 'epoch': 0.23}
{'loss': 0.2999, 'grad_norm': 1.5811758041381836, 'learning_rate': 4.761904761904762e-05, 'epoch': 0.28}
{'loss': 0.2951, 'grad_norm': 1.9880269765853882, 'learning_rate': 5.714285714285714e-05, 'epoch': 0.34}
{'loss': 0.282, 'grad_norm': 1.5736329555511475, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.4}
{'loss': 0.2805, 'grad_norm': 1.3553781509399414, 'learning_rate': 7.619047619047618e-05, 'epoch': 0.45}
{'loss': 0.2717, 'grad_norm': 1.3975526094436646, 'learning_rate': 8.571428571428571e-05, 'epoch': 0.51}
{'loss': 0.2659, 'grad_norm': 1.0928113460540771, 'learning_rate': 9.523809523809524e-05, 'epoch': 0.57}
{'loss': 0.27, 'grad_norm': 1.163079857826233, 'learning_rate': 9.999309273455528e-05, 'epoch': 0.63}
{'loss': 0.2671, 'grad_norm': 0.849062979221344, 'learning_rate': 9.993784606094612e-05, 'epoch': 0.68}
{'loss': 0.2869, 'grad_norm': 0.8933653831481934, 'learning_rate': 9.982741376606078e-05, 'epoch': 0.74}
{'loss': 0.2997, 'grad_norm': 1.046842098236084, 'learning_rate': 9.966191788709716e-05, 'epoch': 0.8}
{'loss': 0.2735, 'grad_norm': 2.4692931175231934, 'learning_rate': 9.944154131125642e-05, 'epoch': 0.85}
{'loss': 0.2905, 'grad_norm': 1.4990626573562622, 'learning_rate': 9.916652757363698e-05, 'epoch': 0.91}
{'loss': 0.258, 'grad_norm': 1.4853521585464478, 'learning_rate': 9.883718058810707e-05, 'epoch': 0.97}


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                                                   [INFO|trainer.py:3410] 2024-07-16 11:22:28,780 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-175
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 11:22:29,531 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 11:22:29,532 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-16 11:22:29,811 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-175/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-16 11:22:29,813 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-175/special_tokens_map.json

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 33%|███▎      | 350/1050 [4:09:10<8:14:50, 42.41s/it][INFO|trainer.py:3719] 2024-07-16 13:25:13,583 >> ***** Running Evaluation *****
[INFO|trainer.py:3721] 2024-07-16 13:25:13,583 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-16 13:25:13,584 >>   Batch size = 1
{'eval_loss': 0.24650488793849945, 'eval_accuracy': 0.909, 'eval_runtime': 206.2691, 'eval_samples_per_second': 12.12, 'eval_steps_per_second': 12.12, 'epoch': 1.0}
{'loss': 0.2471, 'grad_norm': 0.8761231303215027, 'learning_rate': 9.84538643114539e-05, 'epoch': 1.02}
{'loss': 0.2304, 'grad_norm': 1.037913203239441, 'learning_rate': 9.801700234117999e-05, 'epoch': 1.08}
{'loss': 0.2414, 'grad_norm': 0.5963209867477417, 'learning_rate': 9.752707744739145e-05, 'epoch': 1.14}
{'loss': 0.2276, 'grad_norm': 0.8125211596488953, 'learning_rate': 9.698463103929542e-05, 'epoch': 1.19}
{'loss': 0.2137, 'grad_norm': 2.1267828941345215, 'learning_rate': 9.639026256689628e-05, 'epoch': 1.25}
{'loss': 0.2467, 'grad_norm': 3.7273290157318115, 'learning_rate': 9.574462885855174e-05, 'epoch': 1.31}
{'loss': 0.2682, 'grad_norm': 4.215674877166748, 'learning_rate': 9.504844339512095e-05, 'epoch': 1.36}
{'loss': 0.29, 'grad_norm': 1.7884002923965454, 'learning_rate': 9.430247552150673e-05, 'epoch': 1.42}
{'loss': 0.2252, 'grad_norm': 1.312519907951355, 'learning_rate': 9.350754959646306e-05, 'epoch': 1.48}
{'loss': 0.2408, 'grad_norm': 2.8921542167663574, 'learning_rate': 9.266454408160779e-05, 'epoch': 1.54}
{'loss': 0.2171, 'grad_norm': 2.1106109619140625, 'learning_rate': 9.177439057064683e-05, 'epoch': 1.59}
{'loss': 0.2394, 'grad_norm': 1.2954068183898926, 'learning_rate': 9.083807275988284e-05, 'epoch': 1.65}
{'loss': 0.2307, 'grad_norm': 3.02253794670105, 'learning_rate': 8.985662536114613e-05, 'epoch': 1.71}
{'loss': 0.2172, 'grad_norm': 1.063110589981079, 'learning_rate': 8.883113295834892e-05, 'epoch': 1.76}
{'loss': 0.2251, 'grad_norm': 1.7286932468414307, 'learning_rate': 8.776272880892675e-05, 'epoch': 1.82}
{'loss': 0.2435, 'grad_norm': 1.194043755531311, 'learning_rate': 8.665259359149132e-05, 'epoch': 1.88}
{'loss': 0.2425, 'grad_norm': 0.8665173649787903, 'learning_rate': 8.550195410107902e-05, 'epoch': 1.93}
{'loss': 0.2297, 'grad_norm': 1.0586192607879639, 'learning_rate': 8.43120818934367e-05, 'epoch': 1.99}


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                                                   [INFO|trainer.py:3410] 2024-07-16 13:28:39,769 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-350
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 13:28:40,491 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 13:28:40,491 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-16 13:28:40,678 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-350/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-16 13:28:40,680 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-350/special_tokens_map.json

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[INFO|trainer.py:3721] 2024-07-16 15:31:44,177 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-16 15:31:44,177 >>   Batch size = 1
{'eval_loss': 0.24609853327274323, 'eval_accuracy': 0.9031333333333333, 'eval_runtime': 206.1828, 'eval_samples_per_second': 12.125, 'eval_steps_per_second': 12.125, 'epoch': 1.99}
{'loss': 0.1928, 'grad_norm': 1.062510371208191, 'learning_rate': 8.308429187984297e-05, 'epoch': 2.05}
{'loss': 0.194, 'grad_norm': 1.1897845268249512, 'learning_rate': 8.181994087401819e-05, 'epoch': 2.1}
{'loss': 0.1699, 'grad_norm': 1.3862345218658447, 'learning_rate': 8.052042609272817e-05, 'epoch': 2.16}
{'loss': 0.186, 'grad_norm': 0.9099957346916199, 'learning_rate': 7.91871836117395e-05, 'epoch': 2.22}
{'loss': 0.1711, 'grad_norm': 1.0390052795410156, 'learning_rate': 7.782168677883206e-05, 'epoch': 2.27}
{'loss': 0.2012, 'grad_norm': 2.652061700820923, 'learning_rate': 7.642544458562278e-05, 'epoch': 2.33}
{'loss': 0.1898, 'grad_norm': 1.5696091651916504, 'learning_rate': 7.500000000000001e-05, 'epoch': 2.39}
{'loss': 0.2032, 'grad_norm': 0.8261299729347229, 'learning_rate': 7.354692826101102e-05, 'epoch': 2.44}
{'loss': 0.1854, 'grad_norm': 1.252537727355957, 'learning_rate': 7.20678351380872e-05, 'epoch': 2.5}
{'loss': 0.202, 'grad_norm': 1.3897773027420044, 'learning_rate': 7.056435515653059e-05, 'epoch': 2.56}
{'loss': 0.1789, 'grad_norm': 0.8310902714729309, 'learning_rate': 6.903814979122249e-05, 'epoch': 2.62}
{'loss': 0.1969, 'grad_norm': 1.3531970977783203, 'learning_rate': 6.749090563055076e-05, 'epoch': 2.67}
{'loss': 0.1967, 'grad_norm': 1.0367170572280884, 'learning_rate': 6.592433251258423e-05, 'epoch': 2.73}
{'loss': 0.1825, 'grad_norm': 1.2085515260696411, 'learning_rate': 6.434016163555452e-05, 'epoch': 2.79}
{'loss': 0.212, 'grad_norm': 1.2390882968902588, 'learning_rate': 6.274014364473274e-05, 'epoch': 2.84}
{'loss': 0.191, 'grad_norm': 1.4294568300247192, 'learning_rate': 6.112604669781572e-05, 'epoch': 2.9}
{'loss': 0.1792, 'grad_norm': 1.522834300994873, 'learning_rate': 5.949965451095951e-05, 'epoch': 2.96}


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                                                   [INFO|trainer.py:3410] 2024-07-16 15:35:10,893 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-525
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 15:35:11,769 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 15:35:11,770 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-16 15:35:11,956 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-525/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-16 15:35:11,958 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-525/special_tokens_map.json

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 67%|██████▋   | 700/1050 [8:22:12<4:05:38, 42.11s/it][INFO|trainer.py:3719] 2024-07-16 17:38:15,569 >> ***** Running Evaluation *****
[INFO|trainer.py:3721] 2024-07-16 17:38:15,569 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-16 17:38:15,569 >>   Batch size = 1
{'eval_loss': 0.25212058424949646, 'eval_accuracy': 0.9013333333333332, 'eval_runtime': 206.7127, 'eval_samples_per_second': 12.094, 'eval_steps_per_second': 12.094, 'epoch': 2.99}
{'loss': 0.1592, 'grad_norm': 0.655222475528717, 'learning_rate': 5.786276438761927e-05, 'epoch': 3.01}
{'loss': 0.1186, 'grad_norm': 0.8747724294662476, 'learning_rate': 5.621718523237427e-05, 'epoch': 3.07}
{'loss': 0.1195, 'grad_norm': 1.3602584600448608, 'learning_rate': 5.456473555193242e-05, 'epoch': 3.13}
{'loss': 0.144, 'grad_norm': 1.0027141571044922, 'learning_rate': 5.290724144552379e-05, 'epoch': 3.18}
{'loss': 0.1508, 'grad_norm': 2.7845115661621094, 'learning_rate': 5.124653458690365e-05, 'epoch': 3.24}
{'loss': 0.1399, 'grad_norm': 0.7762941718101501, 'learning_rate': 4.9584450200195156e-05, 'epoch': 3.3}
{'loss': 0.1321, 'grad_norm': 0.9395729303359985, 'learning_rate': 4.792282503180867e-05, 'epoch': 3.35}
{'loss': 0.1245, 'grad_norm': 0.9491863250732422, 'learning_rate': 4.626349532067879e-05, 'epoch': 3.41}
{'loss': 0.1211, 'grad_norm': 1.0270098447799683, 'learning_rate': 4.4608294769062075e-05, 'epoch': 3.47}
{'loss': 0.1336, 'grad_norm': 1.0683083534240723, 'learning_rate': 4.295905251613817e-05, 'epoch': 3.53}
{'loss': 0.1379, 'grad_norm': 1.0028445720672607, 'learning_rate': 4.131759111665349e-05, 'epoch': 3.58}
{'loss': 0.1248, 'grad_norm': 0.6078014969825745, 'learning_rate': 3.968572452684113e-05, 'epoch': 3.64}
{'loss': 0.1244, 'grad_norm': 1.0846917629241943, 'learning_rate': 3.806525609984312e-05, 'epoch': 3.7}
{'loss': 0.1455, 'grad_norm': 1.1056978702545166, 'learning_rate': 3.6457976592849754e-05, 'epoch': 3.75}
{'loss': 0.1381, 'grad_norm': 0.8466194868087769, 'learning_rate': 3.486566218815871e-05, 'epoch': 3.81}
{'loss': 0.1278, 'grad_norm': 1.3114445209503174, 'learning_rate': 3.329007253034063e-05, 'epoch': 3.87}
{'loss': 0.1487, 'grad_norm': 1.2287284135818481, 'learning_rate': 3.173294878168025e-05, 'epoch': 3.92}
{'loss': 0.122, 'grad_norm': 0.7370882630348206, 'learning_rate': 3.019601169804216e-05, 'epoch': 3.98}


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                                                   [INFO|trainer.py:3410] 2024-07-16 17:41:43,117 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-700
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 17:41:43,905 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 17:41:43,906 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-16 17:41:44,086 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-700/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-16 17:41:44,087 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-700/special_tokens_map.json

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[INFO|trainer.py:3721] 2024-07-16 19:44:45,913 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-16 19:44:45,913 >>   Batch size = 1
{'eval_loss': 0.27484622597694397, 'eval_accuracy': 0.9049666666666666, 'eval_runtime': 207.5459, 'eval_samples_per_second': 12.046, 'eval_steps_per_second': 12.046, 'epoch': 3.98}
{'loss': 0.1074, 'grad_norm': 0.7140550017356873, 'learning_rate': 2.8680959727287317e-05, 'epoch': 4.04}
{'loss': 0.0856, 'grad_norm': 1.042784333229065, 'learning_rate': 2.718946713234185e-05, 'epoch': 4.09}
{'loss': 0.0837, 'grad_norm': 0.7461434602737427, 'learning_rate': 2.5723182140992387e-05, 'epoch': 4.15}
{'loss': 0.0749, 'grad_norm': 1.004974603652954, 'learning_rate': 2.428372512445233e-05, 'epoch': 4.21}
{'loss': 0.0835, 'grad_norm': 0.6601787805557251, 'learning_rate': 2.2872686806712035e-05, 'epoch': 4.26}
{'loss': 0.0685, 'grad_norm': 0.6331136226654053, 'learning_rate': 2.1491626506651914e-05, 'epoch': 4.32}
{'loss': 0.0787, 'grad_norm': 0.8726811408996582, 'learning_rate': 2.0142070414860704e-05, 'epoch': 4.38}
{'loss': 0.0783, 'grad_norm': 0.8191830515861511, 'learning_rate': 1.8825509907063327e-05, 'epoch': 4.43}
{'loss': 0.0759, 'grad_norm': 0.6537315249443054, 'learning_rate': 1.7543399896022405e-05, 'epoch': 4.49}
{'loss': 0.0952, 'grad_norm': 1.3562214374542236, 'learning_rate': 1.629715722373423e-05, 'epoch': 4.55}
{'loss': 0.0916, 'grad_norm': 1.0490374565124512, 'learning_rate': 1.5088159095696363e-05, 'epoch': 4.61}
{'loss': 0.0874, 'grad_norm': 0.8466753363609314, 'learning_rate': 1.3917741558976894e-05, 'epoch': 4.66}
{'loss': 0.0879, 'grad_norm': 0.9645017385482788, 'learning_rate': 1.2787198025767416e-05, 'epoch': 4.72}
{'loss': 0.0697, 'grad_norm': 3.8121776580810547, 'learning_rate': 1.1697777844051105e-05, 'epoch': 4.78}
{'loss': 0.0873, 'grad_norm': 1.1398985385894775, 'learning_rate': 1.0650684916965559e-05, 'epoch': 4.83}
{'loss': 0.0826, 'grad_norm': 1.19906485080719, 'learning_rate': 9.647076372386194e-06, 'epoch': 4.89}
{'loss': 0.073, 'grad_norm': 0.7644526958465576, 'learning_rate': 8.688061284200266e-06, 'epoch': 4.95}


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                                                   [INFO|trainer.py:3410] 2024-07-16 19:48:12,318 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-875
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 19:48:13,061 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 19:48:13,062 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-16 19:48:13,241 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-875/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-16 19:48:13,242 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-875/special_tokens_map.json

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100%|██████████| 1050/1050 [12:35:30<00:00, 42.34s/it][INFO|trainer.py:3719] 2024-07-16 21:51:33,138 >> ***** Running Evaluation *****
[INFO|trainer.py:3721] 2024-07-16 21:51:33,138 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-16 21:51:33,138 >>   Batch size = 1
{'eval_loss': 0.33086976408958435, 'eval_accuracy': 0.9083, 'eval_runtime': 206.4018, 'eval_samples_per_second': 12.112, 'eval_steps_per_second': 12.112, 'epoch': 4.98}
{'loss': 0.082, 'grad_norm': 0.686549723148346, 'learning_rate': 7.774699446684608e-06, 'epoch': 5.0}
{'loss': 0.0516, 'grad_norm': 0.5072228312492371, 'learning_rate': 6.908000203341802e-06, 'epoch': 5.06}
{'loss': 0.0607, 'grad_norm': 0.5079275965690613, 'learning_rate': 6.088921331488568e-06, 'epoch': 5.12}
{'loss': 0.0451, 'grad_norm': 0.8684201240539551, 'learning_rate': 5.318367983829392e-06, 'epoch': 5.17}
{'loss': 0.0516, 'grad_norm': 0.822227954864502, 'learning_rate': 4.597191688184754e-06, 'epoch': 5.23}
{'loss': 0.0479, 'grad_norm': 0.5135931968688965, 'learning_rate': 3.9261894064796135e-06, 'epoch': 5.29}
{'loss': 0.0547, 'grad_norm': 0.5866013765335083, 'learning_rate': 3.306102654031823e-06, 'epoch': 5.34}
{'loss': 0.0555, 'grad_norm': 1.5747359991073608, 'learning_rate': 2.737616680113758e-06, 'epoch': 5.4}
{'loss': 0.0495, 'grad_norm': 2.3203916549682617, 'learning_rate': 2.221359710692961e-06, 'epoch': 5.46}
{'loss': 0.0522, 'grad_norm': 0.6227638721466064, 'learning_rate': 1.757902254188254e-06, 'epoch': 5.52}
{'loss': 0.0536, 'grad_norm': 1.4704910516738892, 'learning_rate': 1.3477564710088098e-06, 'epoch': 5.57}
{'loss': 0.0518, 'grad_norm': 0.4436222314834595, 'learning_rate': 9.913756075728087e-07, 'epoch': 5.63}
{'loss': 0.0491, 'grad_norm': 0.6575887799263, 'learning_rate': 6.891534954310885e-07, 'epoch': 5.69}
{'loss': 0.0355, 'grad_norm': 0.5118672251701355, 'learning_rate': 4.4142411604936597e-07, 'epoch': 5.74}
{'loss': 0.0425, 'grad_norm': 0.8050460815429688, 'learning_rate': 2.4846123172992954e-07, 'epoch': 5.8}
{'loss': 0.0553, 'grad_norm': 0.6702028512954712, 'learning_rate': 1.1047808308075058e-07, 'epoch': 5.86}
{'loss': 0.061, 'grad_norm': 0.4666765630245209, 'learning_rate': 2.7627153366222013e-08, 'epoch': 5.91}
{'loss': 0.0484, 'grad_norm': 0.5390927195549011, 'learning_rate': 0.0, 'epoch': 5.97}


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                                                   [INFO|trainer.py:3410] 2024-07-16 21:54:59,411 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-1050
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 21:55:00,319 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 21:55:00,320 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-16 21:55:00,504 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-1050/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-16 21:55:00,506 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p1_full/checkpoint-1050/special_tokens_map.json
[INFO|trainer.py:2329] 2024-07-16 21:55:00,991 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)



                                                      

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[INFO|trainer.py:3410] 2024-07-16 21:55:00,996 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p1_full
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 21:55:01,515 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 21:55:01,515 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-16 21:55:01,694 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p1_full/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-16 21:55:01,695 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p1_full/special_tokens_map.json
[INFO|trainer.py:3719] 2024-07-16 21:55:02,127 >> ***** Running Evaluation *****
[INFO|trainer.py:3721] 2024-07-16 21:55:02,127 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-16 21:55:02,128 >>   Batch size = 1
{'eval_loss': 0.43139833211898804, 'eval_accuracy': 0.9057333333333334, 'eval_runtime': 206.2708, 'eval_samples_per_second': 12.12, 'eval_steps_per_second': 12.12, 'epoch': 5.97}
{'train_runtime': 45549.7669, 'train_samples_per_second': 2.964, 'train_steps_per_second': 0.023, 'train_loss': 0.1699013292221796, 'epoch': 5.97}
***** train metrics *****
  epoch                    =       5.9701
  total_flos               = 2841612535GF
  train_loss               =       0.1699
  train_runtime            =  12:39:09.76
  train_samples_per_second =        2.964
  train_steps_per_second   =        0.023
Figure saved at: saves/llama3-8b/lora/sft_bf16_p1_full/training_loss.png
Figure saved at: saves/llama3-8b/lora/sft_bf16_p1_full/training_eval_loss.png
Figure saved at: saves/llama3-8b/lora/sft_bf16_p1_full/training_eval_accuracy.png

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[INFO|modelcard.py:450] 2024-07-16 21:58:29,577 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.9057333333333334}]}
***** eval metrics *****
  epoch                   =     5.9701
  eval_accuracy           =     0.9057
  eval_loss               =     0.4314
  eval_runtime            = 0:03:27.44
  eval_samples_per_second =     12.051
  eval_steps_per_second   =     12.051
wandb: - 0.014 MB of 0.014 MB uploaded
wandb: \ 0.023 MB of 0.063 MB uploaded
wandb: | 0.023 MB of 0.063 MB uploaded
wandb: / 0.063 MB of 0.063 MB uploaded
wandb: 
wandb: Run history:
wandb:           eval/accuracy █▃▁▄▇▅▅
wandb:               eval/loss ▁▁▁▂▄██
wandb:            eval/runtime ▁▁▄█▂▁▇
wandb: eval/samples_per_second ██▅▁▇█▁
wandb:   eval/steps_per_second ██▅▁▇█▁
wandb:             train/epoch ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████
wandb:       train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇████
wandb:         train/grad_norm ▄▁▅▄▃▃▄▃▆▅██▃▃▄▇▂▂▃▄▂▇▂▃▃▂▂▂▂▂▃▂▃▁▂▁▁▁▂▁
wandb:     train/learning_rate ▂▃▅▇██████▇▇▇▇▇▆▆▆▆▅▅▅▄▄▄▃▃▃▃▂▂▂▂▁▁▁▁▁▁▁
wandb:              train/loss █▃▃▃▃▃▃▃▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: 
wandb: Run summary:
wandb:            eval/accuracy 0.90573
wandb:                eval/loss 0.4314
wandb:             eval/runtime 207.4441
wandb:  eval/samples_per_second 12.051
wandb:    eval/steps_per_second 12.051
wandb:               total_flos 3.0511582264790876e+18
wandb:              train/epoch 5.97015
wandb:        train/global_step 1050
wandb:          train/grad_norm 0.53909
wandb:      train/learning_rate 0.0
wandb:               train/loss 0.0484
wandb:               train_loss 0.1699
wandb:            train_runtime 45549.7669
wandb: train_samples_per_second 2.964
wandb:   train_steps_per_second 0.023
wandb: 
wandb: 🚀 View run llama3_8b_p1_full at: https://wandb.ai/inflaton-ai/huggingface/runs/t1nqedbn
wandb: ⭐️ View project at: https://wandb.ai/inflaton-ai/huggingface
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20240716_091552-t1nqedbn/logs
wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information.
Tuning shenzhi-wang/Llama3-8B-Chinese-Chat with config/llama3-8b_lora_sft_bf16-p2.yaml
Current Directory:
/common/home/users/d/dh.huang.2023/code/logical-reasoning/llama-factory
config/llama3-8b_lora_sft_bf16-p2.yaml:
 {
  "model_name_or_path": "shenzhi-wang/Llama3-8B-Chinese-Chat",
  "stage": "sft",
  "do_train": true,
  "finetuning_type": "lora",
  "lora_target": "all",
  "loraplus_lr_ratio": 16.0,
  "upcast_layernorm": true,
  "dataset": "alpaca_mgtv_p2",
  "template": "llama3",
  "cutoff_len": 4096,
  "max_samples": 25000,
  "overwrite_cache": true,
  "preprocessing_num_workers": 16,
  "output_dir": "saves/llama3-8b/lora/sft_bf16_p2_full",
  "logging_steps": 10,
  "save_steps": 175,
  "plot_loss": true,
  "per_device_train_batch_size": 16,
  "gradient_accumulation_steps": 8,
  "learning_rate": 0.0001,
  "num_train_epochs": 6.0,
  "lr_scheduler_type": "cosine",
  "warmup_ratio": 0.1,
  "bf16": true,
  "ddp_timeout": 180000000,
  "val_size": 0.1,
  "per_device_eval_batch_size": 1,
  "eval_strategy": "steps",
  "eval_steps": 175,
  "report_to": "wandb",
  "run_name": "llama3_8b_p2_full"
}
07/16/2024 21:58:48 - INFO - llamafactory.hparams.parser - Resuming training from saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-525.
07/16/2024 21:58:48 - INFO - llamafactory.hparams.parser - Change `output_dir` or use `overwrite_output_dir` to avoid.
07/16/2024 21:58:48 - INFO - llamafactory.hparams.parser - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.bfloat16
[INFO|tokenization_utils_base.py:2108] 2024-07-16 21:59:02,101 >> loading file tokenizer.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/tokenizer.json
[INFO|tokenization_utils_base.py:2108] 2024-07-16 21:59:02,101 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2108] 2024-07-16 21:59:02,101 >> loading file special_tokens_map.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/special_tokens_map.json
[INFO|tokenization_utils_base.py:2108] 2024-07-16 21:59:02,101 >> loading file tokenizer_config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/tokenizer_config.json
[WARNING|logging.py:314] 2024-07-16 21:59:02,314 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
07/16/2024 21:59:02 - INFO - llamafactory.data.template - Replace eos token: <|eot_id|>
07/16/2024 21:59:02 - INFO - llamafactory.data.loader - Loading dataset alpaca_mgtv_p2.json...

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Converting format of dataset (num_proc=16): 100%|██████████| 25000/25000 [00:00<00:00, 94334.66 examples/s]

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input_ids:
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inputs:
<|begin_of_text|><|start_header_id|>user<|end_header_id|>

你是一个情景猜谜游戏的主持人。游戏规则如下：

1. 参与者会得到一个谜面，谜面会描述一个简单又难以理解的事件。
2. 主持人知道谜底，谜底是谜面的答案。
3. 参与者可以询问任何封闭式问题来找寻事件的真相。
4. 对于每个问题，主持人将根据实际情况回答以下五个选项之一：是、不是、不重要、回答正确、问法错误。各回答的判断标准如下：
   - 若谜面和谜底能找到问题的答案，回答：是或者不是
   - 若谜面和谜底不能直接或者间接推断出问题的答案，回答：不重要
   - 若参与者提问不是一个封闭式问题或者问题难以理解，回答：问法错误
   - 若参与者提问基本还原了谜底真相，回答：回答正确
5. 回答中不能添加任何其它信息，也不能省略选项中的任何一个字。例如，不可以把“不是”省略成“不”。

请严格按照这些规则回答参与者提出的问题。

**谜面:** 在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。

**谜底:** 真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。

**参与者提出的问题:** 偷的人信神吗
<|eot_id|><|start_header_id|>assistant<|end_header_id|>

不是<|eot_id|>
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-16 21:59:09,431 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-16 21:59:09,432 >> Model config LlamaConfig {
  "_name_or_path": "shenzhi-wang/Llama3-8B-Chinese-Chat",
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|modeling_utils.py:3474] 2024-07-16 21:59:09,516 >> loading weights file model.safetensors from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/model.safetensors.index.json
[INFO|modeling_utils.py:1519] 2024-07-16 21:59:09,517 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
[INFO|configuration_utils.py:962] 2024-07-16 21:59:09,518 >> Generate config GenerationConfig {
  "bos_token_id": 128000,
  "eos_token_id": 128009
}

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, -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, -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, -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, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 103668, 128009]
labels:
不是<|eot_id|>

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
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Loading checkpoint shards: 100%|██████████| 4/4 [00:08<00:00,  2.22s/it]
[INFO|modeling_utils.py:4280] 2024-07-16 21:59:18,456 >> All model checkpoint weights were used when initializing LlamaForCausalLM.

[INFO|modeling_utils.py:4288] 2024-07-16 21:59:18,456 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at shenzhi-wang/Llama3-8B-Chinese-Chat.
If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
[INFO|configuration_utils.py:917] 2024-07-16 21:59:18,692 >> loading configuration file generation_config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/generation_config.json
[INFO|configuration_utils.py:962] 2024-07-16 21:59:18,693 >> Generate config GenerationConfig {
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "pad_token_id": 128009
}

07/16/2024 21:59:18 - INFO - llamafactory.model.model_utils.checkpointing - Upcasting layernorm weights in float32.
07/16/2024 21:59:18 - INFO - llamafactory.model.model_utils.checkpointing - Gradient checkpointing enabled.
07/16/2024 21:59:18 - INFO - llamafactory.model.model_utils.attention - Using torch SDPA for faster training and inference.
07/16/2024 21:59:18 - INFO - llamafactory.model.adapter - Upcasting trainable params to float32.
07/16/2024 21:59:18 - INFO - llamafactory.model.adapter - Fine-tuning method: LoRA
07/16/2024 21:59:18 - INFO - llamafactory.model.model_utils.misc - Found linear modules: up_proj,v_proj,o_proj,gate_proj,k_proj,down_proj,q_proj
07/16/2024 21:59:19 - INFO - llamafactory.model.loader - trainable params: 20,971,520 || all params: 8,051,232,768 || trainable%: 0.2605
Detected kernel version 4.18.0, 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:641] 2024-07-16 21:59:19,179 >> Using auto half precision backend
[INFO|trainer.py:2461] 2024-07-16 21:59:19,181 >> Loading model from saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-525.
07/16/2024 21:59:19 - INFO - llamafactory.train.trainer_utils - Using LoRA+ optimizer with loraplus lr ratio 16.00.
[INFO|trainer.py:2078] 2024-07-16 21:59:20,044 >> ***** Running training *****
[INFO|trainer.py:2079] 2024-07-16 21:59:20,044 >>   Num examples = 22,500
[INFO|trainer.py:2080] 2024-07-16 21:59:20,044 >>   Num Epochs = 6
[INFO|trainer.py:2081] 2024-07-16 21:59:20,044 >>   Instantaneous batch size per device = 16
[INFO|trainer.py:2084] 2024-07-16 21:59:20,044 >>   Total train batch size (w. parallel, distributed & accumulation) = 128
[INFO|trainer.py:2085] 2024-07-16 21:59:20,044 >>   Gradient Accumulation steps = 8
[INFO|trainer.py:2086] 2024-07-16 21:59:20,044 >>   Total optimization steps = 1,050
[INFO|trainer.py:2087] 2024-07-16 21:59:20,048 >>   Number of trainable parameters = 20,971,520
[INFO|trainer.py:2109] 2024-07-16 21:59:20,049 >>   Continuing training from checkpoint, will skip to saved global_step
[INFO|trainer.py:2110] 2024-07-16 21:59:20,049 >>   Continuing training from epoch 3
[INFO|trainer.py:2111] 2024-07-16 21:59:20,049 >>   Continuing training from global step 525
[INFO|trainer.py:2113] 2024-07-16 21:59:20,049 >>   Will skip the first 3 epochs then the first 0 batches in the first epoch.
[INFO|integration_utils.py:723] 2024-07-16 21:59:20,053 >> Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
wandb: Currently logged in as: inflaton-sg (inflaton-ai). Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.17.4
wandb: Run data is saved locally in /common2/dh.huang.2023/code/logical-reasoning/llama-factory/wandb/run-20240716_215921-b1vc6sbw
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run llama3_8b_p2_full
wandb: ⭐️ View project at https://wandb.ai/inflaton-ai/huggingface
wandb: 🚀 View run at https://wandb.ai/inflaton-ai/huggingface/runs/b1vc6sbw

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 67%|██████▋   | 700/1050 [2:36:51<5:13:03, 53.67s/it][INFO|trainer.py:3719] 2024-07-17 00:36:20,827 >> ***** Running Evaluation *****
[INFO|trainer.py:3721] 2024-07-17 00:36:20,827 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-17 00:36:20,827 >>   Batch size = 1
{'loss': 0.0974, 'grad_norm': 0.8705387711524963, 'learning_rate': 5.786276438761927e-05, 'epoch': 3.03}
{'loss': 0.1113, 'grad_norm': 1.8239766359329224, 'learning_rate': 5.621718523237427e-05, 'epoch': 3.09}
{'loss': 0.1229, 'grad_norm': 1.0920981168746948, 'learning_rate': 5.456473555193242e-05, 'epoch': 3.14}
{'loss': 0.1398, 'grad_norm': 0.7232789993286133, 'learning_rate': 5.290724144552379e-05, 'epoch': 3.2}
{'loss': 0.1621, 'grad_norm': 0.825590193271637, 'learning_rate': 5.124653458690365e-05, 'epoch': 3.26}
{'loss': 0.1444, 'grad_norm': 1.0252315998077393, 'learning_rate': 4.9584450200195156e-05, 'epoch': 3.31}
{'loss': 0.1548, 'grad_norm': 1.245003581047058, 'learning_rate': 4.792282503180867e-05, 'epoch': 3.37}
{'loss': 0.1479, 'grad_norm': 1.084923267364502, 'learning_rate': 4.626349532067879e-05, 'epoch': 3.43}
{'loss': 0.1443, 'grad_norm': 1.438701868057251, 'learning_rate': 4.4608294769062075e-05, 'epoch': 3.48}
{'loss': 0.1482, 'grad_norm': 1.642706036567688, 'learning_rate': 4.295905251613817e-05, 'epoch': 3.54}
{'loss': 0.1474, 'grad_norm': 0.8712331056594849, 'learning_rate': 4.131759111665349e-05, 'epoch': 3.6}
{'loss': 0.1431, 'grad_norm': 1.0291883945465088, 'learning_rate': 3.968572452684113e-05, 'epoch': 3.65}
{'loss': 0.1325, 'grad_norm': 0.9933144450187683, 'learning_rate': 3.806525609984312e-05, 'epoch': 3.71}
{'loss': 0.1558, 'grad_norm': 0.9645794630050659, 'learning_rate': 3.6457976592849754e-05, 'epoch': 3.77}
{'loss': 0.1589, 'grad_norm': 1.2767914533615112, 'learning_rate': 3.486566218815871e-05, 'epoch': 3.82}
{'loss': 0.1463, 'grad_norm': 1.3329861164093018, 'learning_rate': 3.329007253034063e-05, 'epoch': 3.88}
{'loss': 0.1523, 'grad_norm': 1.0121214389801025, 'learning_rate': 3.173294878168025e-05, 'epoch': 3.94}
{'loss': 0.1498, 'grad_norm': 1.350295901298523, 'learning_rate': 3.019601169804216e-05, 'epoch': 4.0}


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                                                   [INFO|trainer.py:3410] 2024-07-17 00:40:26,353 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-700
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-17 00:40:27,886 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-17 00:40:27,887 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-17 00:40:28,093 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-700/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-17 00:40:28,094 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-700/special_tokens_map.json

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[INFO|trainer.py:3721] 2024-07-17 03:17:20,557 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-17 03:17:20,557 >>   Batch size = 1
{'eval_loss': 0.26931771636009216, 'eval_accuracy': 0.9072, 'eval_runtime': 245.5232, 'eval_samples_per_second': 10.182, 'eval_steps_per_second': 10.182, 'epoch': 4.0}
{'loss': 0.1035, 'grad_norm': 1.4912649393081665, 'learning_rate': 2.8680959727287317e-05, 'epoch': 4.05}
{'loss': 0.1001, 'grad_norm': 1.1092299222946167, 'learning_rate': 2.718946713234185e-05, 'epoch': 4.11}
{'loss': 0.095, 'grad_norm': 0.7442717552185059, 'learning_rate': 2.5723182140992387e-05, 'epoch': 4.17}
{'loss': 0.0811, 'grad_norm': 0.8044183254241943, 'learning_rate': 2.428372512445233e-05, 'epoch': 4.22}
{'loss': 0.0838, 'grad_norm': 1.1196448802947998, 'learning_rate': 2.2872686806712035e-05, 'epoch': 4.28}
{'loss': 0.0949, 'grad_norm': 1.1083009243011475, 'learning_rate': 2.1491626506651914e-05, 'epoch': 4.34}
{'loss': 0.095, 'grad_norm': 1.3349604606628418, 'learning_rate': 2.0142070414860704e-05, 'epoch': 4.39}
{'loss': 0.0852, 'grad_norm': 0.8541048765182495, 'learning_rate': 1.8825509907063327e-05, 'epoch': 4.45}
{'loss': 0.0886, 'grad_norm': 0.9618383646011353, 'learning_rate': 1.7543399896022405e-05, 'epoch': 4.51}
{'loss': 0.097, 'grad_norm': 0.684533417224884, 'learning_rate': 1.629715722373423e-05, 'epoch': 4.56}
{'loss': 0.1029, 'grad_norm': 1.2351458072662354, 'learning_rate': 1.5088159095696363e-05, 'epoch': 4.62}
{'loss': 0.1039, 'grad_norm': 1.2963201999664307, 'learning_rate': 1.3917741558976894e-05, 'epoch': 4.68}
{'loss': 0.0964, 'grad_norm': 0.7727164030075073, 'learning_rate': 1.2787198025767416e-05, 'epoch': 4.73}
{'loss': 0.0828, 'grad_norm': 1.4305059909820557, 'learning_rate': 1.1697777844051105e-05, 'epoch': 4.79}
{'loss': 0.0982, 'grad_norm': 1.1239262819290161, 'learning_rate': 1.0650684916965559e-05, 'epoch': 4.85}
{'loss': 0.08, 'grad_norm': 0.9413287043571472, 'learning_rate': 9.647076372386194e-06, 'epoch': 4.9}
{'loss': 0.0825, 'grad_norm': 1.1029815673828125, 'learning_rate': 8.688061284200266e-06, 'epoch': 4.96}


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                                                   [INFO|trainer.py:3410] 2024-07-17 03:21:26,165 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-875
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-17 03:21:27,144 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-17 03:21:27,144 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-17 03:21:27,324 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-875/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-17 03:21:27,325 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-875/special_tokens_map.json

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100%|██████████| 1050/1050 [7:58:41<00:00, 53.52s/it][INFO|trainer.py:3719] 2024-07-17 05:58:11,043 >> ***** Running Evaluation *****
[INFO|trainer.py:3721] 2024-07-17 05:58:11,043 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-17 05:58:11,043 >>   Batch size = 1
{'eval_loss': 0.32899829745292664, 'eval_accuracy': 0.9088, 'eval_runtime': 245.605, 'eval_samples_per_second': 10.179, 'eval_steps_per_second': 10.179, 'epoch': 4.99}
{'loss': 0.0743, 'grad_norm': 0.6363257765769958, 'learning_rate': 7.774699446684608e-06, 'epoch': 5.02}
{'loss': 0.0537, 'grad_norm': 0.5979920029640198, 'learning_rate': 6.908000203341802e-06, 'epoch': 5.08}
{'loss': 0.0585, 'grad_norm': 0.6441344618797302, 'learning_rate': 6.088921331488568e-06, 'epoch': 5.13}
{'loss': 0.0493, 'grad_norm': 0.7351667881011963, 'learning_rate': 5.318367983829392e-06, 'epoch': 5.19}
{'loss': 0.0546, 'grad_norm': 1.1642309427261353, 'learning_rate': 4.597191688184754e-06, 'epoch': 5.25}
{'loss': 0.0576, 'grad_norm': 0.722756028175354, 'learning_rate': 3.9261894064796135e-06, 'epoch': 5.3}
{'loss': 0.0578, 'grad_norm': 0.6322811841964722, 'learning_rate': 3.306102654031823e-06, 'epoch': 5.36}
{'loss': 0.0537, 'grad_norm': 0.5810645818710327, 'learning_rate': 2.737616680113758e-06, 'epoch': 5.42}
{'loss': 0.0505, 'grad_norm': 0.933347225189209, 'learning_rate': 2.221359710692961e-06, 'epoch': 5.47}
{'loss': 0.059, 'grad_norm': 0.9870530962944031, 'learning_rate': 1.757902254188254e-06, 'epoch': 5.53}
{'loss': 0.054, 'grad_norm': 0.7121208906173706, 'learning_rate': 1.3477564710088098e-06, 'epoch': 5.59}
{'loss': 0.058, 'grad_norm': 1.0721352100372314, 'learning_rate': 9.913756075728087e-07, 'epoch': 5.64}
{'loss': 0.0494, 'grad_norm': 0.7931228280067444, 'learning_rate': 6.891534954310885e-07, 'epoch': 5.7}
{'loss': 0.0411, 'grad_norm': 0.6202746629714966, 'learning_rate': 4.4142411604936597e-07, 'epoch': 5.76}
{'loss': 0.0458, 'grad_norm': 0.6872019171714783, 'learning_rate': 2.4846123172992954e-07, 'epoch': 5.81}
{'loss': 0.0637, 'grad_norm': 0.7417372465133667, 'learning_rate': 1.1047808308075058e-07, 'epoch': 5.87}
{'loss': 0.0523, 'grad_norm': 0.6148027777671814, 'learning_rate': 2.7627153366222013e-08, 'epoch': 5.93}
{'loss': 0.0514, 'grad_norm': 0.6213644742965698, 'learning_rate': 0.0, 'epoch': 5.99}


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                                                   [INFO|trainer.py:3410] 2024-07-17 06:02:16,477 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-1050
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-17 06:02:17,272 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-17 06:02:17,273 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-17 06:02:17,455 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-1050/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-17 06:02:17,457 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p2_full/checkpoint-1050/special_tokens_map.json
[INFO|trainer.py:2329] 2024-07-17 06:02:17,944 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)



                                                     

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[INFO|trainer.py:3410] 2024-07-17 06:02:17,949 >> Saving model checkpoint to saves/llama3-8b/lora/sft_bf16_p2_full
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-17 06:02:18,851 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-17 06:02:18,851 >> Model config LlamaConfig {
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|tokenization_utils_base.py:2513] 2024-07-17 06:02:19,033 >> tokenizer config file saved in saves/llama3-8b/lora/sft_bf16_p2_full/tokenizer_config.json
[INFO|tokenization_utils_base.py:2522] 2024-07-17 06:02:19,034 >> Special tokens file saved in saves/llama3-8b/lora/sft_bf16_p2_full/special_tokens_map.json
[INFO|trainer.py:3719] 2024-07-17 06:02:19,449 >> ***** Running Evaluation *****
[INFO|trainer.py:3721] 2024-07-17 06:02:19,449 >>   Num examples = 2500
[INFO|trainer.py:3724] 2024-07-17 06:02:19,449 >>   Batch size = 1
{'eval_loss': 0.4251042604446411, 'eval_accuracy': 0.9049666666666666, 'eval_runtime': 245.4316, 'eval_samples_per_second': 10.186, 'eval_steps_per_second': 10.186, 'epoch': 5.99}
{'train_runtime': 28977.896, 'train_samples_per_second': 4.659, 'train_steps_per_second': 0.036, 'train_loss': 0.04824516418434325, 'epoch': 5.99}
***** train metrics *****
  epoch                    =       5.9851
  total_flos               = 3499703917GF
  train_loss               =       0.0482
  train_runtime            =   8:02:57.89
  train_samples_per_second =        4.659
  train_steps_per_second   =        0.036
Figure saved at: saves/llama3-8b/lora/sft_bf16_p2_full/training_loss.png
Figure saved at: saves/llama3-8b/lora/sft_bf16_p2_full/training_eval_loss.png
Figure saved at: saves/llama3-8b/lora/sft_bf16_p2_full/training_eval_accuracy.png

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[INFO|modelcard.py:450] 2024-07-17 06:06:26,923 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.9049666666666666}]}
***** eval metrics *****
  epoch                   =     5.9851
  eval_accuracy           =      0.905
  eval_loss               =     0.4251
  eval_runtime            = 0:04:07.46
  eval_samples_per_second =     10.102
  eval_steps_per_second   =     10.102
wandb: - 0.014 MB of 0.014 MB uploaded
wandb: \ 0.014 MB of 0.059 MB uploaded
wandb: | 0.059 MB of 0.059 MB uploaded
wandb: 
wandb: Run history:
wandb:           eval/accuracy ▅█▁▁
wandb:               eval/loss ▁▄██
wandb:            eval/runtime ▁▂▁█
wandb: eval/samples_per_second █▇█▁
wandb:   eval/steps_per_second █▇█▁
wandb:             train/epoch ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇█████
wandb:       train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇█████
wandb:         train/grad_norm ▃█▄▂▄▅▆▇▃▃▃▅▃▅▆▂▂▄▅▃▃▅▅▂▄▃▄▁▁▂▂▁▁▃▂▄▁▂▂▁
wandb:     train/learning_rate ███▇▇▇▆▆▆▆▅▅▅▅▄▄▄▄▃▃▃▃▃▃▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁
wandb:              train/loss ▄▅▆█▇█▇▇▇▆██▇▇▅▄▃▃▄▄▄▅▅▄▄▃▃▂▂▁▂▂▂▂▂▂▁▁▂▂
wandb: 
wandb: Run summary:
wandb:            eval/accuracy 0.90497
wandb:                eval/loss 0.4251
wandb:             eval/runtime 247.467
wandb:  eval/samples_per_second 10.102
wandb:    eval/steps_per_second 10.102
wandb:               total_flos 3.757778467594961e+18
wandb:              train/epoch 5.98507
wandb:        train/global_step 1050
wandb:          train/grad_norm 0.62136
wandb:      train/learning_rate 0.0
wandb:               train/loss 0.0514
wandb:               train_loss 0.04825
wandb:            train_runtime 28977.896
wandb: train_samples_per_second 4.659
wandb:   train_steps_per_second 0.036
wandb: 
wandb: 🚀 View run llama3_8b_p2_full at: https://wandb.ai/inflaton-ai/huggingface/runs/b1vc6sbw
wandb: ⭐️ View project at: https://wandb.ai/inflaton-ai/huggingface
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20240716_215921-b1vc6sbw/logs
wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information.
Current Directory:
/common/home/users/d/dh.huang.2023/code/logical-reasoning
Wed Jul 17 06:06:35 2024       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.90.07              Driver Version: 550.90.07      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA L40                     On  |   00000000:01:00.0 Off |                    0 |
| N/A   61C    P0             96W /  300W |       1MiB /  46068MiB |      5%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+
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L3 cache:            32768K
NUMA node0 CPU(s):   0-127
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
MemTotal:       527669560 kB
Eval shenzhi-wang/Llama3-8B-Chinese-Chat with llama-factory/saves/llama3-8b/lora/sft_bf16_p1_full
[INFO|tokenization_utils_base.py:2108] 2024-07-17 06:06:46,704 >> loading file tokenizer.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/tokenizer.json
[INFO|tokenization_utils_base.py:2108] 2024-07-17 06:06:46,704 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2108] 2024-07-17 06:06:46,704 >> loading file special_tokens_map.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/special_tokens_map.json
[INFO|tokenization_utils_base.py:2108] 2024-07-17 06:06:46,704 >> loading file tokenizer_config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/tokenizer_config.json
[WARNING|logging.py:314] 2024-07-17 06:06:47,238 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
/common/home/users/d/dh.huang.2023/.conda/envs/llm-perf-bench/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:733] 2024-07-17 06:06:47,472 >> loading configuration file config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/config.json
[INFO|configuration_utils.py:796] 2024-07-17 06:06:47,472 >> Model config LlamaConfig {
  "_name_or_path": "shenzhi-wang/Llama3-8B-Chinese-Chat",
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "max_position_embeddings": 8192,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "num_key_value_heads": 8,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-05,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "vocab_size": 128256
}

[INFO|modeling_utils.py:3474] 2024-07-17 06:06:47,556 >> loading weights file model.safetensors from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/model.safetensors.index.json
[INFO|modeling_utils.py:1519] 2024-07-17 06:06:47,564 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.
[INFO|configuration_utils.py:962] 2024-07-17 06:06:47,565 >> Generate config GenerationConfig {
  "bos_token_id": 128000,
  "eos_token_id": 128009
}


Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
Loading checkpoint shards:  25%|██▌       | 1/4 [00:05<00:17,  5.76s/it]
Loading checkpoint shards:  50%|█████     | 2/4 [00:11<00:11,  5.57s/it]
Loading checkpoint shards:  75%|███████▌  | 3/4 [00:16<00:05,  5.51s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [00:18<00:00,  3.87s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [00:18<00:00,  4.50s/it]
[INFO|modeling_utils.py:4280] 2024-07-17 06:07:05,768 >> All model checkpoint weights were used when initializing LlamaForCausalLM.

[INFO|modeling_utils.py:4288] 2024-07-17 06:07:05,768 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at shenzhi-wang/Llama3-8B-Chinese-Chat.
If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.
[INFO|configuration_utils.py:917] 2024-07-17 06:07:06,019 >> loading configuration file generation_config.json from cache at /common/scratch/users/d/dh.huang.2023/transformers/hub/models--shenzhi-wang--Llama3-8B-Chinese-Chat/snapshots/f25f13cb2571e70e285121faceac92926b51e6f5/generation_config.json
[INFO|configuration_utils.py:962] 2024-07-17 06:07:06,019 >> Generate config GenerationConfig {
  "bos_token_id": 128000,
  "eos_token_id": 128009,
  "pad_token_id": 128009
}

srun: Job step aborted: Waiting up to 17 seconds for job step to finish.

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  0%|          | 5/3000 [2:03:05<1228:53:29, 1477.13s/it]slurmstepd: error: *** STEP 71584.0 ON lexicon CANCELLED AT 2024-07-17T08:13:52 ***
slurmstepd: error: *** JOB 71584 ON lexicon CANCELLED AT 2024-07-17T08:13:52 ***
Job ID: 71584
Cluster: crimson
User/Group: dh.huang.2023/dh.huang.2023
State: CANCELLED (exit code 0)
Nodes: 1
Cores per node: 10
CPU Utilized: 20:12:53
CPU Efficiency: 8.80% of 9-13:48:20 core-walltime
Job Wall-clock time: 22:58:50
Memory Utilized: 12.92 GB
Memory Efficiency: 5.05% of 256.00 GB