WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
model training desc:  使用随机选择的关键句训练
2023-12-06 10:34:27.251 | INFO     | __main__:init_components:108 - Initializing components...
model training desc:  使用随机选择的关键句训练
2023-12-06 10:34:27.257 | INFO     | __main__:init_components:108 - Initializing components...
You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. If you see this, DO NOT PANIC! This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
2023-12-06 10:34:47.365 | INFO     | __main__:init_components:143 - 

2023-12-06 10:34:47.365 | INFO     | __main__:init_components:144 - ********************
2023-12-06 10:34:47.365 | INFO     | __main__:init_components:145 - using TechGPT-7B
2023-12-06 10:34:47.365 | INFO     | __main__:init_components:146 - ********************
2023-12-06 10:34:47.365 | INFO     | __main__:init_components:147 - 

memory footprint of model: 5.472740173339844 GB
You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. If you see this, DO NOT PANIC! This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
2023-12-06 10:34:48.089 | INFO     | __main__:init_components:143 - 

2023-12-06 10:34:48.090 | INFO     | __main__:init_components:144 - ********************
2023-12-06 10:34:48.090 | INFO     | __main__:init_components:145 - using TechGPT-7B
2023-12-06 10:34:48.090 | INFO     | __main__:init_components:146 - ********************
2023-12-06 10:34:48.090 | INFO     | __main__:init_components:147 - 

memory footprint of model: 5.472740173339844 GB
trainable params: 319,815,680 || all params: 7,447,007,232 || trainable%: 4.294553100818044
2023-12-06 10:34:50.010 | INFO     | component.dataset:__init__:14 - Loading data: /data0/maqi/KGLQA-data/datasets/NCR/random_select/ncr_chunk_1400_instruct/train.jsonl
2023-12-06 10:34:50.112 | INFO     | component.dataset:__init__:19 - there are 15319 data in dataset
2023-12-06 10:34:50.170 | INFO     | __main__:main:231 - *** starting training ***
trainable params: 319,815,680 || all params: 7,447,007,232 || trainable%: 4.294553100818044
2023-12-06 10:34:50.758 | INFO     | component.dataset:__init__:14 - Loading data: /data0/maqi/KGLQA-data/datasets/NCR/random_select/ncr_chunk_1400_instruct/train.jsonl
2023-12-06 10:34:50.857 | INFO     | component.dataset:__init__:19 - there are 15319 data in dataset
2023-12-06 10:34:50.875 | INFO     | __main__:main:231 - *** starting training ***

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{'loss': 5.7171, 'learning_rate': 1.652173913043478e-05, 'global_step': 20, 'epoch': 0.05}

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{'loss': 3.3801, 'learning_rate': 3.304347826086956e-05, 'global_step': 40, 'epoch': 0.1}

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{'loss': 0.7521, 'learning_rate': 4.956521739130435e-05, 'global_step': 60, 'epoch': 0.16}

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{'loss': 0.7016, 'learning_rate': 6.695652173913044e-05, 'global_step': 80, 'epoch': 0.21}

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{'loss': 0.7053, 'learning_rate': 8.434782608695653e-05, 'global_step': 100, 'epoch': 0.26}

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{'loss': 0.7202, 'learning_rate': 0.0001, 'global_step': 120, 'epoch': 0.31}

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{'loss': 0.7062, 'learning_rate': 0.0001, 'global_step': 140, 'epoch': 0.37}

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{'loss': 0.709, 'learning_rate': 0.0001, 'global_step': 160, 'epoch': 0.42}

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{'loss': 0.6773, 'learning_rate': 0.0001, 'global_step': 180, 'epoch': 0.47}

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{'loss': 0.6675, 'learning_rate': 0.0001, 'global_step': 200, 'epoch': 0.52}

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{'loss': 0.6582, 'learning_rate': 0.0001, 'global_step': 220, 'epoch': 0.57}

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{'loss': 0.7473, 'learning_rate': 0.0001, 'global_step': 240, 'epoch': 0.63}

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{'loss': 0.6401, 'learning_rate': 0.0001, 'global_step': 260, 'epoch': 0.68}

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{'loss': 0.6312, 'learning_rate': 0.0001, 'global_step': 280, 'epoch': 0.73}

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{'loss': 0.6399, 'learning_rate': 0.0001, 'global_step': 300, 'epoch': 0.78}

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{'loss': 0.6484, 'learning_rate': 0.0001, 'global_step': 320, 'epoch': 0.84}

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{'loss': 0.6193, 'learning_rate': 0.0001, 'global_step': 340, 'epoch': 0.89}

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{'loss': 0.5908, 'learning_rate': 0.0001, 'global_step': 360, 'epoch': 0.94}

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{'loss': 0.6047, 'learning_rate': 0.0001, 'global_step': 380, 'epoch': 0.99}

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{'loss': 0.5931, 'learning_rate': 0.0001, 'global_step': 400, 'epoch': 1.04}

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{'loss': 0.6038, 'learning_rate': 0.0001, 'global_step': 420, 'epoch': 1.1}

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{'loss': 0.5726, 'learning_rate': 0.0001, 'global_step': 440, 'epoch': 1.15}

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{'loss': 0.5738, 'learning_rate': 0.0001, 'global_step': 460, 'epoch': 1.2}

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{'loss': 0.5536, 'learning_rate': 0.0001, 'global_step': 480, 'epoch': 1.25}

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{'loss': 0.576, 'learning_rate': 0.0001, 'global_step': 500, 'epoch': 1.31}

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{'loss': 0.5598, 'learning_rate': 0.0001, 'global_step': 520, 'epoch': 1.36}

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{'loss': 0.547, 'learning_rate': 0.0001, 'global_step': 540, 'epoch': 1.41}

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{'loss': 0.5269, 'learning_rate': 0.0001, 'global_step': 560, 'epoch': 1.46}

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{'loss': 0.5244, 'learning_rate': 0.0001, 'global_step': 580, 'epoch': 1.51}

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{'loss': 0.5017, 'learning_rate': 0.0001, 'global_step': 600, 'epoch': 1.57}

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{'loss': 0.557, 'learning_rate': 0.0001, 'global_step': 620, 'epoch': 1.62}

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{'loss': 0.4926, 'learning_rate': 0.0001, 'global_step': 640, 'epoch': 1.67}

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{'loss': 0.4458, 'learning_rate': 0.0001, 'global_step': 660, 'epoch': 1.72}

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{'loss': 0.4503, 'learning_rate': 0.0001, 'global_step': 680, 'epoch': 1.78}

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{'loss': 0.4413, 'learning_rate': 0.0001, 'global_step': 700, 'epoch': 1.83}

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{'loss': 0.4396, 'learning_rate': 0.0001, 'global_step': 720, 'epoch': 1.88}

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{'loss': 0.3788, 'learning_rate': 0.0001, 'global_step': 740, 'epoch': 1.93}

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{'loss': 0.3731, 'learning_rate': 0.0001, 'global_step': 760, 'epoch': 1.98}

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{'loss': 0.3797, 'learning_rate': 0.0001, 'global_step': 780, 'epoch': 2.04}

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{'loss': 0.4084, 'learning_rate': 0.0001, 'global_step': 800, 'epoch': 2.09}

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{'loss': 0.3583, 'learning_rate': 0.0001, 'global_step': 820, 'epoch': 2.14}

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{'loss': 0.3262, 'learning_rate': 0.0001, 'global_step': 840, 'epoch': 2.19}

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{'loss': 0.3407, 'learning_rate': 0.0001, 'global_step': 860, 'epoch': 2.25}

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{'loss': 0.3476, 'learning_rate': 0.0001, 'global_step': 880, 'epoch': 2.3}

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{'loss': 0.3041, 'learning_rate': 0.0001, 'global_step': 900, 'epoch': 2.35}

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{'loss': 0.3037, 'learning_rate': 0.0001, 'global_step': 920, 'epoch': 2.4}

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{'loss': 0.2546, 'learning_rate': 0.0001, 'global_step': 940, 'epoch': 2.45}

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{'loss': 0.2807, 'learning_rate': 0.0001, 'global_step': 960, 'epoch': 2.51}

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{'loss': 0.2338, 'learning_rate': 0.0001, 'global_step': 980, 'epoch': 2.56}

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{'loss': 0.3036, 'learning_rate': 0.0001, 'global_step': 1000, 'epoch': 2.61}

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{'loss': 0.2492, 'learning_rate': 0.0001, 'global_step': 1020, 'epoch': 2.66}

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{'loss': 0.2118, 'learning_rate': 0.0001, 'global_step': 1040, 'epoch': 2.72}

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{'loss': 0.2155, 'learning_rate': 0.0001, 'global_step': 1060, 'epoch': 2.77}

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{'loss': 0.2032, 'learning_rate': 0.0001, 'global_step': 1080, 'epoch': 2.82}

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{'loss': 0.1983, 'learning_rate': 0.0001, 'global_step': 1100, 'epoch': 2.87}

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{'loss': 0.1879, 'learning_rate': 0.0001, 'global_step': 1120, 'epoch': 2.92}

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{'loss': 0.1938, 'learning_rate': 0.0001, 'global_step': 1140, 'epoch': 2.98}

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{'train_runtime': 37640.6404, 'train_samples_per_second': 1.221, 'train_steps_per_second': 0.031, 'train_loss': 0.6198576812229538, 'epoch': 3.0}

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***** train metrics *****
  epoch                    =         3.0
  train_loss               =      0.6199
  train_runtime            = 10:27:20.64
  train_samples_per_second =       1.221
  train_steps_per_second   =       0.031