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[2023-12-10 15:26:50,373] torch.distributed.run: [WARNING] master_addr is only used for static rdzv_backend and when rdzv_endpoint is not specified.
[2023-12-10 15:26:50,373] torch.distributed.run: [WARNING]
[2023-12-10 15:26:50,373] torch.distributed.run: [WARNING] *****************************************
[2023-12-10 15:26:50,373] torch.distributed.run: [WARNING] 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.
[2023-12-10 15:26:50,373] torch.distributed.run: [WARNING] *****************************************
12/10/2023 15:26:54 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
12/10/2023 15:26:54 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=2,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=0.0001,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=output/text-20231210-152648-1e-4/runs/Dec10_15-26-53_lily-gpu07,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=1.0,
logging_strategy=steps,
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=500,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_torch,
optim_args=None,
output_dir=output/text-20231210-152648-1e-4,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=1,
predict_with_generate=False,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=output/text-20231210-152648-1e-4,
save_on_each_node=False,
save_safetensors=False,
save_steps=50,
save_strategy=steps,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
[INFO|tokenization_utils_base.py:2043] 2023-12-10 15:26:55,236 >> loading file tokenizer.model from cache at /home/haiyue/.cache/huggingface/hub/models--THUDM--chatglm3-6b-base/snapshots/f91a1de587fdc692073367198e65369669a0b49d/tokenizer.model
[INFO|tokenization_utils_base.py:2043] 2023-12-10 15:26:55,236 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2043] 2023-12-10 15:26:55,236 >> loading file special_tokens_map.json from cache at None
[INFO|tokenization_utils_base.py:2043] 2023-12-10 15:26:55,236 >> loading file tokenizer_config.json from cache at /home/haiyue/.cache/huggingface/hub/models--THUDM--chatglm3-6b-base/snapshots/f91a1de587fdc692073367198e65369669a0b49d/tokenizer_config.json
[INFO|tokenization_utils_base.py:2043] 2023-12-10 15:26:55,236 >> loading file tokenizer.json from cache at None
[INFO|configuration_utils.py:715] 2023-12-10 15:26:55,589 >> loading configuration file config.json from cache at /home/haiyue/.cache/huggingface/hub/models--THUDM--chatglm3-6b-base/snapshots/f91a1de587fdc692073367198e65369669a0b49d/config.json
[INFO|configuration_utils.py:715] 2023-12-10 15:26:55,852 >> loading configuration file config.json from cache at /home/haiyue/.cache/huggingface/hub/models--THUDM--chatglm3-6b-base/snapshots/f91a1de587fdc692073367198e65369669a0b49d/config.json
[INFO|configuration_utils.py:775] 2023-12-10 15:26:55,853 >> Model config ChatGLMConfig {
"_name_or_path": "THUDM/chatglm3-6b-base",
"add_bias_linear": false,
"add_qkv_bias": true,
"apply_query_key_layer_scaling": true,
"apply_residual_connection_post_layernorm": false,
"architectures": [
"ChatGLMModel"
],
"attention_dropout": 0.0,
"attention_softmax_in_fp32": true,
"auto_map": {
"AutoConfig": "THUDM/chatglm3-6b-base--configuration_chatglm.ChatGLMConfig",
"AutoModel": "THUDM/chatglm3-6b-base--modeling_chatglm.ChatGLMForConditionalGeneration",
"AutoModelForCausalLM": "THUDM/chatglm3-6b-base--modeling_chatglm.ChatGLMForConditionalGeneration",
"AutoModelForSeq2SeqLM": "THUDM/chatglm3-6b-base--modeling_chatglm.ChatGLMForConditionalGeneration",
"AutoModelForSequenceClassification": "THUDM/chatglm3-6b-base--modeling_chatglm.ChatGLMForSequenceClassification"
},
"bias_dropout_fusion": true,
"classifier_dropout": null,
"eos_token_id": 2,
"ffn_hidden_size": 13696,
"fp32_residual_connection": false,
"hidden_dropout": 0.0,
"hidden_size": 4096,
"kv_channels": 128,
"layernorm_epsilon": 1e-05,
"model_type": "chatglm",
"multi_query_attention": true,
"multi_query_group_num": 2,
"num_attention_heads": 32,
"num_layers": 28,
"original_rope": true,
"pad_token_id": 0,
"padded_vocab_size": 65024,
"post_layer_norm": true,
"pre_seq_len": null,
"prefix_projection": false,
"quantization_bit": 0,
"rmsnorm": true,
"seq_length": 32768,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.34.0",
"use_cache": true,
"vocab_size": 65024
}
12/10/2023 15:26:55 - WARNING - __main__ - Process rank: 1, device: cuda:1, n_gpu: 1distributed training: True, 16-bits training: False
[INFO|modeling_utils.py:2993] 2023-12-10 15:26:56,183 >> loading weights file pytorch_model.bin from cache at /home/haiyue/.cache/huggingface/hub/models--THUDM--chatglm3-6b-base/snapshots/f91a1de587fdc692073367198e65369669a0b49d/pytorch_model.bin.index.json
[INFO|configuration_utils.py:770] 2023-12-10 15:26:56,185 >> Generate config GenerationConfig {
"eos_token_id": 2,
"pad_token_id": 0
}
Loading checkpoint shards: 0%| | 0/7 [00:00<?, ?it/s] Loading checkpoint shards: 0%| | 0/7 [00:00<?, ?it/s] Loading checkpoint shards: 14%|█▍ | 1/7 [00:01<00:10, 1.83s/it] Loading checkpoint shards: 14%|█▍ | 1/7 [00:01<00:11, 1.85s/it] Loading checkpoint shards: 29%|██▊ | 2/7 [00:03<00:09, 1.83s/it] Loading checkpoint shards: 29%|██▊ | 2/7 [00:03<00:09, 1.91s/it] Loading checkpoint shards: 43%|████▎ | 3/7 [00:05<00:07, 1.98s/it] Loading checkpoint shards: 43%|████▎ | 3/7 [00:05<00:07, 1.99s/it] Loading checkpoint shards: 57%|█████▋ | 4/7 [00:07<00:05, 1.98s/it] Loading checkpoint shards: 57%|█████▋ | 4/7 [00:07<00:05, 1.95s/it] Loading checkpoint shards: 71%|███████▏ | 5/7 [00:10<00:04, 2.08s/it] Loading checkpoint shards: 71%|███████▏ | 5/7 [00:09<00:03, 1.93s/it] Loading checkpoint shards: 86%|████████▌ | 6/7 [00:12<00:02, 2.07s/it] Loading checkpoint shards: 86%|████████▌ | 6/7 [00:11<00:01, 1.93s/it] Loading checkpoint shards: 100%|██████████| 7/7 [00:13<00:00, 1.79s/it] Loading checkpoint shards: 100%|██████████| 7/7 [00:13<00:00, 1.90s/it]
[INFO|modeling_utils.py:3775] 2023-12-10 15:27:09,563 >> All model checkpoint weights were used when initializing ChatGLMForConditionalGeneration.
[INFO|modeling_utils.py:3783] 2023-12-10 15:27:09,564 >> All the weights of ChatGLMForConditionalGeneration were initialized from the model checkpoint at THUDM/chatglm3-6b-base.
If your task is similar to the task the model of the checkpoint was trained on, you can already use ChatGLMForConditionalGeneration for predictions without further training.
[INFO|modeling_utils.py:3352] 2023-12-10 15:27:09,818 >> Generation config file not found, using a generation config created from the model config.
Loading checkpoint shards: 100%|██████████| 7/7 [00:12<00:00, 1.64s/it] Loading checkpoint shards: 100%|██████████| 7/7 [00:12<00:00, 1.81s/it]
Train dataset size: 52002
Sanity Check >>>>>>>>>>>>>
'[gMASK]': 64790 -> -100
'sop': 64792 -> -100
'Instruction': 29101 -> -100
':': 30954 -> -100
'Give': 10465 -> -100
'three': 1194 -> -100
'tips': 6639 -> -100
'for': 332 -> -100
'staying': 10061 -> -100
'healthy': 4651 -> -100
'.': 30930 -> -100
'\n': 13 -> -100
'An': 4244 -> -100
'sw': 1902 -> -100
'er': 266 -> -100
':': 30954 -> -100
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'at': 269 -> 269
'a': 260 -> 260
'balanced': 12949 -> 12949
'diet': 5546 -> 5546
'and': 293 -> 293
'make': 794 -> 794
'sure': 1506 -> 1506
'to': 289 -> 289
'include': 1860 -> 1860
'plenty': 5765 -> 5765
'of': 290 -> 290
'fruits': 13665 -> 13665
'and': 293 -> 293
'vegetables': 11567 -> 11567
'.': 30930 -> 30930
'': 30910 -> 30910
'\n': 13 -> 13
'2': 30943 -> 30943
'.': 30930 -> 30930
'Exercise': 23340 -> 23340
'regularly': 7414 -> 7414
'to': 289 -> 289
'keep': 1407 -> 1407
'your': 475 -> 475
'body': 1934 -> 1934
'active': 4047 -> 4047
'and': 293 -> 293
'strong': 2034 -> 2034
'.': 30930 -> 30930
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'\n': 13 -> 13
'3': 30966 -> 30966
'.': 30930 -> 30930
'Get': 3286 -> 3286
'enough': 1775 -> 1775
'sleep': 4039 -> 4039
'and': 293 -> 293
'maintain': 3165 -> 3165
'a': 260 -> 260
'consistent': 7096 -> 7096
'sleep': 4039 -> 4039
'schedule': 5821 -> 5821
'.': 30930 -> 30930
'': 2 -> 2
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<<<<<<<<<<<<< Sanity Check
Train dataset size: 52002
Sanity Check >>>>>>>>>>>>>
'[gMASK]': 64790 -> -100
'sop': 64792 -> -100
'Instruction': 29101 -> -100
':': 30954 -> -100
'Give': 10465 -> -100
'three': 1194 -> -100
'tips': 6639 -> -100
'for': 332 -> -100
'staying': 10061 -> -100
'healthy': 4651 -> -100
'.': 30930 -> -100
'\n': 13 -> -100
'An': 4244 -> -100
'sw': 1902 -> -100
'er': 266 -> -100
':': 30954 -> -100
'': 30910 -> -100
'': 30910 -> 30910
'1': 30939 -> 30939
'.': 30930 -> 30930
'E': 30950 -> 30950
'at': 269 -> 269
'a': 260 -> 260
'balanced': 12949 -> 12949
'diet': 5546 -> 5546
'and': 293 -> 293
'make': 794 -> 794
'sure': 1506 -> 1506
'to': 289 -> 289
'include': 1860 -> 1860
'plenty': 5765 -> 5765
'of': 290 -> 290
'fruits': 13665 -> 13665
'and': 293 -> 293
'vegetables': 11567 -> 11567
'.': 30930 -> 30930
'': 30910 -> 30910
'\n': 13 -> 13
'2': 30943 -> 30943
'.': 30930 -> 30930
'Exercise': 23340 -> 23340
'regularly': 7414 -> 7414
'to': 289 -> 289
'keep': 1407 -> 1407
'your': 475 -> 475
'body': 1934 -> 1934
'active': 4047 -> 4047
'and': 293 -> 293
'strong': 2034 -> 2034
'.': 30930 -> 30930
'': 30910 -> 30910
'\n': 13 -> 13
'3': 30966 -> 30966
'.': 30930 -> 30930
'Get': 3286 -> 3286
'enough': 1775 -> 1775
'sleep': 4039 -> 4039
'and': 293 -> 293
'maintain': 3165 -> 3165
'a': 260 -> 260
'consistent': 7096 -> 7096
'sleep': 4039 -> 4039
'schedule': 5821 -> 5821
'.': 30930 -> 30930
'': 2 -> 2
'': 0 -> -100
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<<<<<<<<<<<<< Sanity Check
[INFO|trainer.py:576] 2023-12-10 15:27:18,453 >> max_steps is given, it will override any value given in num_train_epochs
[INFO|trainer.py:1760] 2023-12-10 15:27:20,364 >> ***** Running training *****
[INFO|trainer.py:1761] 2023-12-10 15:27:20,364 >> Num examples = 52,002
[INFO|trainer.py:1762] 2023-12-10 15:27:20,364 >> Num Epochs = 1
[INFO|trainer.py:1763] 2023-12-10 15:27:20,364 >> Instantaneous batch size per device = 1
[INFO|trainer.py:1766] 2023-12-10 15:27:20,364 >> Total train batch size (w. parallel, distributed & accumulation) = 4
[INFO|trainer.py:1767] 2023-12-10 15:27:20,365 >> Gradient Accumulation steps = 2
[INFO|trainer.py:1768] 2023-12-10 15:27:20,365 >> Total optimization steps = 500
[INFO|trainer.py:1769] 2023-12-10 15:27:20,366 >> Number of trainable parameters = 1,949,696
0%| | 0/500 [00:00<?, ?it/s][W reducer.cpp:1346] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1346] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
0%| | 1/500 [00:02<22:59, 2.76s/it] {'loss': 1.4542, 'learning_rate': 9.98e-05, 'epoch': 0.0}
0%| | 1/500 [00:02<22:59, 2.76s/it] 0%| | 2/500 [00:03<12:28, 1.50s/it] {'loss': 0.0, 'learning_rate': 9.960000000000001e-05, 'epoch': 0.0}
0%| | 2/500 [00:03<12:28, 1.50s/it] 1%| | 3/500 [00:04<09:28, 1.14s/it] {'loss': 0.0, 'learning_rate': 9.94e-05, 'epoch': 0.0}
1%| | 3/500 [00:04<09:28, 1.14s/it] 1%| | 4/500 [00:04<07:36, 1.09it/s] {'loss': 0.0, 'learning_rate': 9.92e-05, 'epoch': 0.0}
1%| | 4/500 [00:04<07:36, 1.09it/s] 1%| | 5/500 [00:05<06:33, 1.26it/s] {'loss': 0.0, 'learning_rate': 9.900000000000001e-05, 'epoch': 0.0}
1%| | 5/500 [00:05<06:33, 1.26it/s] 1%| | 6/500 [00:05<06:17, 1.31it/s] {'loss': 0.0, 'learning_rate': 9.88e-05, 'epoch': 0.0}
1%| | 6/500 [00:05<06:17, 1.31it/s] 1%|▏ | 7/500 [00:06<05:57, 1.38it/s] {'loss': 0.0, 'learning_rate': 9.86e-05, 'epoch': 0.0}
1%|▏ | 7/500 [00:06<05:57, 1.38it/s] 2%|▏ | 8/500 [00:07<06:11, 1.32it/s] {'loss': 0.0, 'learning_rate': 9.84e-05, 'epoch': 0.0}
2%|▏ | 8/500 [00:07<06:11, 1.32it/s] 2%|▏ | 9/500 [00:08<05:58, 1.37it/s] {'loss': 0.0, 'learning_rate': 9.82e-05, 'epoch': 0.0}
2%|▏ | 9/500 [00:08<05:58, 1.37it/s] 2%|▏ | 10/500 [00:08<05:45, 1.42it/s] {'loss': 0.0, 'learning_rate': 9.8e-05, 'epoch': 0.0}
2%|▏ | 10/500 [00:08<05:45, 1.42it/s] 2%|▏ | 11/500 [00:09<06:06, 1.33it/s] {'loss': 0.0, 'learning_rate': 9.78e-05, 'epoch': 0.0}
2%|▏ | 11/500 [00:09<06:06, 1.33it/s] 2%|▏ | 12/500 [00:10<06:39, 1.22it/s] {'loss': 0.0, 'learning_rate': 9.76e-05, 'epoch': 0.0}
2%|▏ | 12/500 [00:10<06:39, 1.22it/s] 3%|▎ | 13/500 [00:11<07:58, 1.02it/s] {'loss': 0.0, 'learning_rate': 9.74e-05, 'epoch': 0.0}
3%|▎ | 13/500 [00:11<07:58, 1.02it/s] 3%|▎ | 14/500 [00:13<08:59, 1.11s/it] {'loss': 0.0, 'learning_rate': 9.72e-05, 'epoch': 0.0}
3%|▎ | 14/500 [00:13<08:59, 1.11s/it] 3%|▎ | 15/500 [00:14<09:43, 1.20s/it] {'loss': 0.0, 'learning_rate': 9.7e-05, 'epoch': 0.0}
3%|▎ | 15/500 [00:14<09:43, 1.20s/it] 3%|▎ | 16/500 [00:16<10:14, 1.27s/it] {'loss': 0.0, 'learning_rate': 9.680000000000001e-05, 'epoch': 0.0}
3%|▎ | 16/500 [00:16<10:14, 1.27s/it] 3%|▎ | 17/500 [00:17<10:37, 1.32s/it] {'loss': 0.0, 'learning_rate': 9.66e-05, 'epoch': 0.0}
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6%|▌ | 29/500 [00:34<11:09, 1.42s/it] 6%|▌ | 30/500 [00:36<11:04, 1.41s/it] {'loss': 0.0, 'learning_rate': 9.4e-05, 'epoch': 0.0}
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7%|▋ | 33/500 [00:40<10:48, 1.39s/it] 7%|▋ | 34/500 [00:41<10:57, 1.41s/it] {'loss': 0.0, 'learning_rate': 9.320000000000002e-05, 'epoch': 0.0}
7%|▋ | 34/500 [00:41<10:57, 1.41s/it] 7%|▋ | 35/500 [00:43<10:56, 1.41s/it] {'loss': 0.0, 'learning_rate': 9.300000000000001e-05, 'epoch': 0.0}
7%|▋ | 35/500 [00:43<10:56, 1.41s/it] 7%|▋ | 36/500 [00:44<10:55, 1.41s/it] {'loss': 0.0, 'learning_rate': 9.28e-05, 'epoch': 0.0}
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7%|▋ | 37/500 [00:45<10:56, 1.42s/it] 8%|▊ | 38/500 [00:47<10:53, 1.42s/it] {'loss': 0.0, 'learning_rate': 9.240000000000001e-05, 'epoch': 0.0}
8%|▊ | 38/500 [00:47<10:53, 1.42s/it] 8%|▊ | 39/500 [00:48<10:54, 1.42s/it] {'loss': 0.0, 'learning_rate': 9.22e-05, 'epoch': 0.0}
8%|▊ | 39/500 [00:48<10:54, 1.42s/it] 8%|▊ | 40/500 [00:50<10:56, 1.43s/it] {'loss': 0.0, 'learning_rate': 9.200000000000001e-05, 'epoch': 0.0}
8%|▊ | 40/500 [00:50<10:56, 1.43s/it] 8%|▊ | 41/500 [00:51<10:54, 1.43s/it] {'loss': 0.0, 'learning_rate': 9.180000000000001e-05, 'epoch': 0.0}
8%|▊ | 41/500 [00:51<10:54, 1.43s/it] 8%|▊ | 42/500 [00:53<10:53, 1.43s/it] {'loss': 0.0, 'learning_rate': 9.16e-05, 'epoch': 0.0}
8%|▊ | 42/500 [00:53<10:53, 1.43s/it] 9%|▊ | 43/500 [00:54<10:50, 1.42s/it] {'loss': 0.0, 'learning_rate': 9.140000000000001e-05, 'epoch': 0.0}
9%|▊ | 43/500 [00:54<10:50, 1.42s/it] 9%|▉ | 44/500 [00:55<10:53, 1.43s/it] {'loss': 0.0, 'learning_rate': 9.120000000000001e-05, 'epoch': 0.0}
9%|▉ | 44/500 [00:55<10:53, 1.43s/it] 9%|▉ | 45/500 [00:57<10:53, 1.44s/it] {'loss': 0.0, 'learning_rate': 9.1e-05, 'epoch': 0.0}
9%|▉ | 45/500 [00:57<10:53, 1.44s/it] 9%|▉ | 46/500 [00:58<10:57, 1.45s/it] {'loss': 0.0, 'learning_rate': 9.080000000000001e-05, 'epoch': 0.0}
9%|▉ | 46/500 [00:58<10:57, 1.45s/it] 9%|▉ | 47/500 [01:00<10:59, 1.46s/it] {'loss': 0.0, 'learning_rate': 9.06e-05, 'epoch': 0.0}
9%|▉ | 47/500 [01:00<10:59, 1.46s/it] 10%|▉ | 48/500 [01:01<10:55, 1.45s/it] {'loss': 0.0, 'learning_rate': 9.04e-05, 'epoch': 0.0}
10%|▉ | 48/500 [01:01<10:55, 1.45s/it] 10%|▉ | 49/500 [01:03<10:57, 1.46s/it] {'loss': 0.0, 'learning_rate': 9.020000000000001e-05, 'epoch': 0.0}
10%|▉ | 49/500 [01:03<10:57, 1.46s/it] 10%|█ | 50/500 [01:04<10:51, 1.45s/it] {'loss': 0.0, 'learning_rate': 9e-05, 'epoch': 0.0}
10%|█ | 50/500 [01:04<10:51, 1.45s/it][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:28:26,862 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-50/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:28:26,862 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-50/special_tokens_map.json
10%|█ | 51/500 [01:06<11:00, 1.47s/it] {'loss': 0.0, 'learning_rate': 8.98e-05, 'epoch': 0.0}
10%|█ | 51/500 [01:06<11:00, 1.47s/it] 10%|█ | 52/500 [01:07<11:01, 1.48s/it] {'loss': 0.0, 'learning_rate': 8.960000000000001e-05, 'epoch': 0.0}
10%|█ | 52/500 [01:07<11:01, 1.48s/it] 11%|█ | 53/500 [01:09<10:57, 1.47s/it] {'loss': 0.0, 'learning_rate': 8.94e-05, 'epoch': 0.0}
11%|█ | 53/500 [01:09<10:57, 1.47s/it] 11%|█ | 54/500 [01:10<10:57, 1.47s/it] {'loss': 0.0, 'learning_rate': 8.92e-05, 'epoch': 0.0}
11%|█ | 54/500 [01:10<10:57, 1.47s/it] 11%|█ | 55/500 [01:12<10:52, 1.47s/it] {'loss': 0.0, 'learning_rate': 8.900000000000001e-05, 'epoch': 0.0}
11%|█ | 55/500 [01:12<10:52, 1.47s/it] 11%|█ | 56/500 [01:13<10:48, 1.46s/it] {'loss': 0.0, 'learning_rate': 8.88e-05, 'epoch': 0.0}
11%|█ | 56/500 [01:13<10:48, 1.46s/it] 11%|█▏ | 57/500 [01:14<10:42, 1.45s/it] {'loss': 0.0, 'learning_rate': 8.86e-05, 'epoch': 0.0}
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12%|█▏ | 61/500 [01:18<07:51, 1.07s/it] 12%|█▏ | 62/500 [01:19<07:29, 1.03s/it] {'loss': 0.0, 'learning_rate': 8.76e-05, 'epoch': 0.0}
12%|█▏ | 62/500 [01:19<07:29, 1.03s/it] 13%|█▎ | 63/500 [01:20<07:11, 1.01it/s] {'loss': 0.0, 'learning_rate': 8.740000000000001e-05, 'epoch': 0.0}
13%|█▎ | 63/500 [01:20<07:11, 1.01it/s] 13%|█▎ | 64/500 [01:21<06:57, 1.05it/s] {'loss': 0.0, 'learning_rate': 8.72e-05, 'epoch': 0.0}
13%|█▎ | 64/500 [01:21<06:57, 1.05it/s] 13%|█▎ | 65/500 [01:22<06:52, 1.05it/s] {'loss': 0.0, 'learning_rate': 8.7e-05, 'epoch': 0.0}
13%|█▎ | 65/500 [01:22<06:52, 1.05it/s] 13%|█▎ | 66/500 [01:23<07:08, 1.01it/s] {'loss': 0.0, 'learning_rate': 8.680000000000001e-05, 'epoch': 0.01}
13%|█▎ | 66/500 [01:23<07:08, 1.01it/s] 13%|█▎ | 67/500 [01:24<07:43, 1.07s/it] {'loss': 0.0, 'learning_rate': 8.66e-05, 'epoch': 0.01}
13%|█▎ | 67/500 [01:24<07:43, 1.07s/it] 14%|█▎ | 68/500 [01:26<08:16, 1.15s/it] {'loss': 0.0, 'learning_rate': 8.64e-05, 'epoch': 0.01}
14%|█▎ | 68/500 [01:26<08:16, 1.15s/it] 14%|█▍ | 69/500 [01:27<08:38, 1.20s/it] {'loss': 0.0, 'learning_rate': 8.620000000000001e-05, 'epoch': 0.01}
14%|█▍ | 69/500 [01:27<08:38, 1.20s/it] 14%|█▍ | 70/500 [01:28<08:32, 1.19s/it] {'loss': 0.0, 'learning_rate': 8.6e-05, 'epoch': 0.01}
14%|█▍ | 70/500 [01:28<08:32, 1.19s/it] 14%|█▍ | 71/500 [01:29<07:31, 1.05s/it] {'loss': 0.0, 'learning_rate': 8.58e-05, 'epoch': 0.01}
14%|█▍ | 71/500 [01:29<07:31, 1.05s/it] 14%|█▍ | 72/500 [01:29<06:12, 1.15it/s] {'loss': 0.0, 'learning_rate': 8.560000000000001e-05, 'epoch': 0.01}
14%|█▍ | 72/500 [01:29<06:12, 1.15it/s] 15%|█▍ | 73/500 [01:30<05:17, 1.34it/s] {'loss': 0.0, 'learning_rate': 8.54e-05, 'epoch': 0.01}
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19%|█▉ | 97/500 [01:42<04:17, 1.57it/s] 20%|█▉ | 98/500 [01:43<05:47, 1.16it/s] {'loss': 0.0, 'learning_rate': 8.04e-05, 'epoch': 0.01}
20%|█▉ | 98/500 [01:43<05:47, 1.16it/s] 20%|█▉ | 99/500 [01:44<06:58, 1.04s/it] {'loss': 0.0, 'learning_rate': 8.020000000000001e-05, 'epoch': 0.01}
20%|█▉ | 99/500 [01:44<06:58, 1.04s/it] 20%|██ | 100/500 [01:46<07:36, 1.14s/it] {'loss': 0.0, 'learning_rate': 8e-05, 'epoch': 0.01}
20%|██ | 100/500 [01:46<07:36, 1.14s/it][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:29:08,477 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-100/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:29:08,478 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-100/special_tokens_map.json
20%|██ | 101/500 [01:47<07:13, 1.09s/it] {'loss': 0.0, 'learning_rate': 7.98e-05, 'epoch': 0.01}
20%|██ | 101/500 [01:47<07:13, 1.09s/it] 20%|██ | 102/500 [01:48<06:56, 1.05s/it] {'loss': 0.0, 'learning_rate': 7.960000000000001e-05, 'epoch': 0.01}
20%|██ | 102/500 [01:48<06:56, 1.05s/it] 21%|██ | 103/500 [01:49<06:42, 1.01s/it] {'loss': 0.0, 'learning_rate': 7.94e-05, 'epoch': 0.01}
21%|██ | 103/500 [01:49<06:42, 1.01s/it] 21%|██ | 104/500 [01:50<06:31, 1.01it/s] {'loss': 0.0, 'learning_rate': 7.920000000000001e-05, 'epoch': 0.01}
21%|██ | 104/500 [01:50<06:31, 1.01it/s] 21%|██ | 105/500 [01:50<06:20, 1.04it/s] {'loss': 0.0, 'learning_rate': 7.900000000000001e-05, 'epoch': 0.01}
21%|██ | 105/500 [01:50<06:20, 1.04it/s] 21%|██ | 106/500 [01:51<05:52, 1.12it/s] {'loss': 0.0, 'learning_rate': 7.88e-05, 'epoch': 0.01}
21%|██ | 106/500 [01:51<05:52, 1.12it/s] 21%|██▏ | 107/500 [01:52<05:32, 1.18it/s] {'loss': 0.0, 'learning_rate': 7.860000000000001e-05, 'epoch': 0.01}
21%|██▏ | 107/500 [01:52<05:32, 1.18it/s] 22%|██▏ | 108/500 [01:53<05:36, 1.16it/s] {'loss': 0.0, 'learning_rate': 7.840000000000001e-05, 'epoch': 0.01}
22%|██▏ | 108/500 [01:53<05:36, 1.16it/s] 22%|██▏ | 109/500 [01:54<05:25, 1.20it/s] {'loss': 0.0, 'learning_rate': 7.82e-05, 'epoch': 0.01}
22%|██▏ | 109/500 [01:54<05:25, 1.20it/s] 22%|██▏ | 110/500 [01:54<05:10, 1.26it/s] {'loss': 0.0, 'learning_rate': 7.800000000000001e-05, 'epoch': 0.01}
22%|██▏ | 110/500 [01:54<05:10, 1.26it/s] 22%|██▏ | 111/500 [01:55<05:12, 1.25it/s] {'loss': 0.0, 'learning_rate': 7.780000000000001e-05, 'epoch': 0.01}
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30%|███ | 150/500 [02:47<08:08, 1.40s/it][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:30:09,868 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-150/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:30:09,869 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-150/special_tokens_map.json
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40%|████ | 200/500 [03:37<03:13, 1.55it/s][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:31:00,119 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-200/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:31:00,120 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-200/special_tokens_map.json
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50%|█████ | 250/500 [04:20<05:51, 1.41s/it][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:31:42,260 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-250/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:31:42,261 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-250/special_tokens_map.json
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60%|██████ | 300/500 [05:26<04:02, 1.21s/it][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:32:48,867 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-300/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:32:48,867 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-300/special_tokens_map.json
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70%|███████ | 350/500 [06:05<02:25, 1.03it/s][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:33:27,722 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-350/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:33:27,723 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-350/special_tokens_map.json
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80%|████████ | 400/500 [07:09<01:17, 1.29it/s][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:34:31,811 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-400/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:34:31,811 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-400/special_tokens_map.json
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90%|█████████ | 450/500 [08:00<00:45, 1.10it/s][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:35:22,531 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-450/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:35:22,531 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-450/special_tokens_map.json
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100%|██████████| 500/500 [08:54<00:00, 1.13it/s][INFO|tokenization_utils_base.py:2437] 2023-12-10 15:36:16,994 >> tokenizer config file saved in output/text-20231210-152648-1e-4/checkpoint-500/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:36:16,994 >> Special tokens file saved in output/text-20231210-152648-1e-4/checkpoint-500/special_tokens_map.json
[INFO|trainer.py:2017] 2023-12-10 15:36:17,040 >>
Training completed. Do not forget to share your model on huggingface.co/models =)
{'train_runtime': 536.6749, 'train_samples_per_second': 3.727, 'train_steps_per_second': 0.932, 'train_loss': 0.002908447265625, 'epoch': 0.04}
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[INFO|tokenization_utils_base.py:2437] 2023-12-10 15:36:17,061 >> tokenizer config file saved in output/text-20231210-152648-1e-4/tokenizer_config.json
[INFO|tokenization_utils_base.py:2446] 2023-12-10 15:36:17,061 >> Special tokens file saved in output/text-20231210-152648-1e-4/special_tokens_map.json