The following values were not passed to `accelerate launch` and had defaults used instead:
	`--num_processes` was set to a value of `4`
		More than one GPU was found, enabling multi-GPU training.
		If this was unintended please pass in `--num_processes=1`.
	`--num_machines` was set to a value of `1`
	`--mixed_precision` was set to a value of `'no'`
	`--dynamo_backend` was set to a value of `'no'`
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
gradient_accumulation_steps:  4
gradient_accumulation_steps:  4
gradient_accumulation_steps:  4
Params using prompt template alpaca:
base_model: baichuan-inc/Baichuan2-7B-Base
data_path: ../../data/belle_dolphine/p11.jsonl
output_dir: ../out/lora/p11
batch_size: 32
micro_batch_size: 2
num_epochs: 1
learning_rate: 0.0004
cutoff_len: 4096
val_set_size: 0
lr_scheduler: cosine
warmup_steps: 100
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: ['gate_proj', 'down_proj', 'up_proj']
train_on_inputs: False
add_eos_token: False
group_by_length: False
wandb_project: lora-moe
wandb_run_name: belle_dolphine-p11
wandb_watch: 
wandb_log_model: 
resume_from_checkpoint: False

gradient_accumulation_steps:  4

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pre-trained model's BOS EOS and PAD token id: 1 2 0  => It should be 1 2 None

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pre-trained model's BOS EOS and PAD token id: 1 2 0  => It should be 1 2 None
pre-trained model's BOS EOS and PAD token id: 1 2 0  => It should be 1 2 None

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trainable params: 23,199,744 || all params: 7,529,172,992 || trainable%: 0.30813137146205183
trainable params: 23,199,744 || all params: 7,529,172,992 || trainable%: 0.30813137146205183

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Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
  warnings.warn(
/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
  warnings.warn(
/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
  warnings.warn(
wandb: Currently logged in as: k-duan (nus_keyu). Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.16.1
wandb: Run data is saved locally in /home/aiops/duanky/lora-moe/experiments/launch_scripts/wandb/run-20231229_062717-koei9tvj
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run belle_dolphine-p11
wandb: ⭐️ View project at https://wandb.ai/nus_keyu/lora-moe
wandb: 🚀 View run at https://wandb.ai/nus_keyu/lora-moe/runs/koei9tvj

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  warnings.warn(

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{'loss': 1.4831, 'learning_rate': 0.000284, 'epoch': 0.03}

  3%|▎         | 71/2117 [02:25<1:09:57,  2.05s/it]
  3%|▎         | 72/2117 [02:26<1:08:14,  2.00s/it]
                                                   
{'loss': 1.3906, 'learning_rate': 0.000288, 'epoch': 0.03}

  3%|▎         | 72/2117 [02:26<1:08:14,  2.00s/it]
  3%|▎         | 73/2117 [02:28<1:05:57,  1.94s/it]
                                                   
{'loss': 1.2823, 'learning_rate': 0.000292, 'epoch': 0.03}

  3%|▎         | 73/2117 [02:28<1:05:57,  1.94s/it]
  3%|▎         | 74/2117 [02:30<1:06:35,  1.96s/it]
                                                   
{'loss': 1.3114, 'learning_rate': 0.000296, 'epoch': 0.03}

  3%|▎         | 74/2117 [02:30<1:06:35,  1.96s/it]
  4%|▎         | 75/2117 [02:32<1:07:12,  1.97s/it]
                                                   
{'loss': 1.8664, 'learning_rate': 0.00030000000000000003, 'epoch': 0.04}

  4%|▎         | 75/2117 [02:32<1:07:12,  1.97s/it]
  4%|▎         | 76/2117 [02:34<1:05:43,  1.93s/it]
                                                   
{'loss': 1.3636, 'learning_rate': 0.000304, 'epoch': 0.04}

  4%|▎         | 76/2117 [02:34<1:05:43,  1.93s/it]
  4%|▎         | 77/2117 [02:36<1:05:31,  1.93s/it]
                                                   
{'loss': 1.5702, 'learning_rate': 0.000308, 'epoch': 0.04}

  4%|▎         | 77/2117 [02:36<1:05:31,  1.93s/it]
  4%|▎         | 78/2117 [02:38<1:08:36,  2.02s/it]
                                                   
{'loss': 1.5398, 'learning_rate': 0.00031200000000000005, 'epoch': 0.04}

  4%|▎         | 78/2117 [02:38<1:08:36,  2.02s/it]
  4%|▎         | 79/2117 [02:40<1:07:45,  2.00s/it]
                                                   
{'loss': 1.3273, 'learning_rate': 0.00031600000000000004, 'epoch': 0.04}

  4%|▎         | 79/2117 [02:40<1:07:45,  2.00s/it]
  4%|▍         | 80/2117 [02:42<1:09:53,  2.06s/it]
                                                   
{'loss': 1.6198, 'learning_rate': 0.00032, 'epoch': 0.04}

  4%|▍         | 80/2117 [02:42<1:09:53,  2.06s/it]
  4%|▍         | 81/2117 [02:44<1:04:57,  1.91s/it]
                                                   
{'loss': 1.3984, 'learning_rate': 0.000324, 'epoch': 0.04}

  4%|▍         | 81/2117 [02:44<1:04:57,  1.91s/it]
  4%|▍         | 82/2117 [02:46<1:05:21,  1.93s/it]
                                                   
{'loss': 1.4277, 'learning_rate': 0.000328, 'epoch': 0.04}

  4%|▍         | 82/2117 [02:46<1:05:21,  1.93s/it]
  4%|▍         | 83/2117 [02:48<1:05:25,  1.93s/it]
                                                   
{'loss': 1.3518, 'learning_rate': 0.000332, 'epoch': 0.04}

  4%|▍         | 83/2117 [02:48<1:05:25,  1.93s/it]
  4%|▍         | 84/2117 [02:50<1:08:54,  2.03s/it]
                                                   
{'loss': 1.6, 'learning_rate': 0.000336, 'epoch': 0.04}

  4%|▍         | 84/2117 [02:50<1:08:54,  2.03s/it]
  4%|▍         | 85/2117 [02:52<1:08:14,  2.01s/it]
                                                   
{'loss': 1.6434, 'learning_rate': 0.00034, 'epoch': 0.04}

  4%|▍         | 85/2117 [02:52<1:08:14,  2.01s/it]
  4%|▍         | 86/2117 [02:54<1:06:20,  1.96s/it]
                                                   
{'loss': 1.4813, 'learning_rate': 0.000344, 'epoch': 0.04}

  4%|▍         | 86/2117 [02:54<1:06:20,  1.96s/it]
  4%|▍         | 87/2117 [02:56<1:06:58,  1.98s/it]
                                                   
{'loss': 1.7128, 'learning_rate': 0.000348, 'epoch': 0.04}

  4%|▍         | 87/2117 [02:56<1:06:58,  1.98s/it]
  4%|▍         | 88/2117 [02:58<1:05:33,  1.94s/it]
                                                   
{'loss': 1.1899, 'learning_rate': 0.00035200000000000005, 'epoch': 0.04}

  4%|▍         | 88/2117 [02:58<1:05:33,  1.94s/it]
  4%|▍         | 89/2117 [03:00<1:04:47,  1.92s/it]
                                                   
{'loss': 1.6948, 'learning_rate': 0.00035600000000000003, 'epoch': 0.04}

  4%|▍         | 89/2117 [03:00<1:04:47,  1.92s/it]
  4%|▍         | 90/2117 [03:02<1:08:07,  2.02s/it]
                                                   
{'loss': 1.3608, 'learning_rate': 0.00036, 'epoch': 0.04}

  4%|▍         | 90/2117 [03:02<1:08:07,  2.02s/it]
  4%|▍         | 91/2117 [03:04<1:06:12,  1.96s/it]
                                                   
{'loss': 1.4245, 'learning_rate': 0.000364, 'epoch': 0.04}

  4%|▍         | 91/2117 [03:04<1:06:12,  1.96s/it]
  4%|▍         | 92/2117 [03:06<1:05:13,  1.93s/it]
                                                   
{'loss': 1.4884, 'learning_rate': 0.00036800000000000005, 'epoch': 0.04}

  4%|▍         | 92/2117 [03:06<1:05:13,  1.93s/it]
  4%|▍         | 93/2117 [03:08<1:05:42,  1.95s/it]
                                                   
{'loss': 1.4071, 'learning_rate': 0.00037200000000000004, 'epoch': 0.04}

  4%|▍         | 93/2117 [03:08<1:05:42,  1.95s/it]
  4%|▍         | 94/2117 [03:10<1:05:55,  1.96s/it]
                                                   
{'loss': 1.4908, 'learning_rate': 0.000376, 'epoch': 0.04}

  4%|▍         | 94/2117 [03:10<1:05:55,  1.96s/it]
  4%|▍         | 95/2117 [03:11<1:04:15,  1.91s/it]
                                                   
{'loss': 1.564, 'learning_rate': 0.00038, 'epoch': 0.04}

  4%|▍         | 95/2117 [03:11<1:04:15,  1.91s/it]
  5%|▍         | 96/2117 [03:13<1:04:00,  1.90s/it]
                                                   
{'loss': 1.3262, 'learning_rate': 0.000384, 'epoch': 0.05}

  5%|▍         | 96/2117 [03:13<1:04:00,  1.90s/it]
  5%|▍         | 97/2117 [03:16<1:08:14,  2.03s/it]
                                                   
{'loss': 1.4975, 'learning_rate': 0.000388, 'epoch': 0.05}

  5%|▍         | 97/2117 [03:16<1:08:14,  2.03s/it]
  5%|▍         | 98/2117 [03:18<1:08:53,  2.05s/it]
                                                   
{'loss': 1.1834, 'learning_rate': 0.000392, 'epoch': 0.05}

  5%|▍         | 98/2117 [03:18<1:08:53,  2.05s/it]
  5%|▍         | 99/2117 [03:19<1:05:52,  1.96s/it]
                                                   
{'loss': 1.4296, 'learning_rate': 0.00039600000000000003, 'epoch': 0.05}

  5%|▍         | 99/2117 [03:19<1:05:52,  1.96s/it]
  5%|▍         | 100/2117 [03:21<1:05:48,  1.96s/it]
                                                    
{'loss': 1.5633, 'learning_rate': 0.0004, 'epoch': 0.05}

  5%|▍         | 100/2117 [03:21<1:05:48,  1.96s/it]
  5%|▍         | 101/2117 [03:23<1:07:04,  2.00s/it]
                                                    
{'loss': 1.5139, 'learning_rate': 0.00039999975740163973, 'epoch': 0.05}

  5%|▍         | 101/2117 [03:23<1:07:04,  2.00s/it]
  5%|▍         | 102/2117 [03:25<1:05:47,  1.96s/it]
                                                    
{'loss': 1.3188, 'learning_rate': 0.0003999990296071474, 'epoch': 0.05}

  5%|▍         | 102/2117 [03:25<1:05:47,  1.96s/it]
  5%|▍         | 103/2117 [03:27<1:07:14,  2.00s/it]
                                                    
{'loss': 1.495, 'learning_rate': 0.0003999978166182887, 'epoch': 0.05}

  5%|▍         | 103/2117 [03:27<1:07:14,  2.00s/it]
  5%|▍         | 104/2117 [03:29<1:06:10,  1.97s/it]
                                                    
{'loss': 1.6301, 'learning_rate': 0.0003999961184380062, 'epoch': 0.05}

  5%|▍         | 104/2117 [03:29<1:06:10,  1.97s/it]
  5%|▍         | 105/2117 [03:32<1:09:04,  2.06s/it]
                                                    
{'loss': 1.5732, 'learning_rate': 0.0003999939350704197, 'epoch': 0.05}

  5%|▍         | 105/2117 [03:32<1:09:04,  2.06s/it]
  5%|▌         | 106/2117 [03:34<1:07:49,  2.02s/it]
                                                    
{'loss': 1.5114, 'learning_rate': 0.0003999912665208261, 'epoch': 0.05}

  5%|▌         | 106/2117 [03:34<1:07:49,  2.02s/it]
  5%|▌         | 107/2117 [03:36<1:08:13,  2.04s/it]
                                                    
{'loss': 1.3505, 'learning_rate': 0.0003999881127956992, 'epoch': 0.05}

  5%|▌         | 107/2117 [03:36<1:08:13,  2.04s/it]
  5%|▌         | 108/2117 [03:38<1:08:41,  2.05s/it]
                                                    
{'loss': 1.4632, 'learning_rate': 0.0003999844739026899, 'epoch': 0.05}

  5%|▌         | 108/2117 [03:38<1:08:41,  2.05s/it]
  5%|▌         | 109/2117 [03:40<1:07:36,  2.02s/it]
                                                    
{'loss': 1.5271, 'learning_rate': 0.00039998034985062604, 'epoch': 0.05}

  5%|▌         | 109/2117 [03:40<1:07:36,  2.02s/it]
  5%|▌         | 110/2117 [03:42<1:07:40,  2.02s/it]
                                                    
{'loss': 1.3881, 'learning_rate': 0.0003999757406495126, 'epoch': 0.05}

  5%|▌         | 110/2117 [03:42<1:07:40,  2.02s/it]
  5%|▌         | 111/2117 [03:43<1:04:48,  1.94s/it]
                                                    
{'loss': 1.2799, 'learning_rate': 0.00039997064631053133, 'epoch': 0.05}

  5%|▌         | 111/2117 [03:43<1:04:48,  1.94s/it]
  5%|▌         | 112/2117 [03:45<1:05:55,  1.97s/it]
                                                    
{'loss': 1.5678, 'learning_rate': 0.0003999650668460411, 'epoch': 0.05}

  5%|▌         | 112/2117 [03:46<1:05:55,  1.97s/it]
  5%|▌         | 113/2117 [03:47<1:05:56,  1.97s/it]
                                                    
{'loss': 1.1719, 'learning_rate': 0.00039995900226957745, 'epoch': 0.05}

  5%|▌         | 113/2117 [03:47<1:05:56,  1.97s/it]
  5%|▌         | 114/2117 [03:49<1:04:34,  1.93s/it]
                                                    
{'loss': 1.4742, 'learning_rate': 0.0003999524525958531, 'epoch': 0.05}

  5%|▌         | 114/2117 [03:49<1:04:34,  1.93s/it]
  5%|▌         | 115/2117 [03:52<1:07:54,  2.04s/it]
                                                    
{'loss': 1.3277, 'learning_rate': 0.00039994541784075746, 'epoch': 0.05}

  5%|▌         | 115/2117 [03:52<1:07:54,  2.04s/it]
  5%|▌         | 116/2117 [03:53<1:04:25,  1.93s/it]
                                                    
{'loss': 1.2949, 'learning_rate': 0.00039993789802135655, 'epoch': 0.05}

  5%|▌         | 116/2117 [03:53<1:04:25,  1.93s/it]
  6%|▌         | 117/2117 [03:56<1:12:36,  2.18s/it]
                                                    
{'loss': 1.7286, 'learning_rate': 0.0003999298931558936, 'epoch': 0.06}

  6%|▌         | 117/2117 [03:56<1:12:36,  2.18s/it]
  6%|▌         | 118/2117 [03:58<1:11:17,  2.14s/it]
                                                    
{'loss': 1.7081, 'learning_rate': 0.00039992140326378794, 'epoch': 0.06}

  6%|▌         | 118/2117 [03:58<1:11:17,  2.14s/it]
  6%|▌         | 119/2117 [04:00<1:10:46,  2.13s/it]
                                                    
{'loss': 1.4714, 'learning_rate': 0.0003999124283656362, 'epoch': 0.06}

  6%|▌         | 119/2117 [04:00<1:10:46,  2.13s/it]
  6%|▌         | 120/2117 [04:02<1:10:18,  2.11s/it]
                                                    
{'loss': 1.3633, 'learning_rate': 0.0003999029684832112, 'epoch': 0.06}

  6%|▌         | 120/2117 [04:02<1:10:18,  2.11s/it]
  6%|▌         | 121/2117 [04:04<1:09:46,  2.10s/it]
                                                    
{'loss': 1.3825, 'learning_rate': 0.0003998930236394625, 'epoch': 0.06}

  6%|▌         | 121/2117 [04:04<1:09:46,  2.10s/it]
  6%|▌         | 122/2117 [04:06<1:09:07,  2.08s/it]
                                                    
{'loss': 1.5665, 'learning_rate': 0.0003998825938585162, 'epoch': 0.06}

  6%|▌         | 122/2117 [04:06<1:09:07,  2.08s/it]
  6%|▌         | 123/2117 [04:08<1:08:39,  2.07s/it]
                                                    
{'loss': 1.2232, 'learning_rate': 0.00039987167916567454, 'epoch': 0.06}

  6%|▌         | 123/2117 [04:08<1:08:39,  2.07s/it]
  6%|▌         | 124/2117 [04:11<1:09:42,  2.10s/it]
                                                    
{'loss': 1.5348, 'learning_rate': 0.0003998602795874167, 'epoch': 0.06}

  6%|▌         | 124/2117 [04:11<1:09:42,  2.10s/it]
  6%|▌         | 125/2117 [04:12<1:07:29,  2.03s/it]
                                                    
{'loss': 1.4528, 'learning_rate': 0.00039984839515139767, 'epoch': 0.06}

  6%|▌         | 125/2117 [04:12<1:07:29,  2.03s/it]
  6%|▌         | 126/2117 [04:14<1:03:52,  1.92s/it]
                                                    
{'loss': 1.4307, 'learning_rate': 0.00039983602588644884, 'epoch': 0.06}

  6%|▌         | 126/2117 [04:14<1:03:52,  1.92s/it]
  6%|▌         | 127/2117 [04:16<1:02:05,  1.87s/it]
                                                    
{'loss': 1.246, 'learning_rate': 0.000399823171822578, 'epoch': 0.06}

  6%|▌         | 127/2117 [04:16<1:02:05,  1.87s/it]
  6%|▌         | 128/2117 [04:18<1:01:04,  1.84s/it]
                                                    
{'loss': 1.1974, 'learning_rate': 0.0003998098329909689, 'epoch': 0.06}

  6%|▌         | 128/2117 [04:18<1:01:04,  1.84s/it]
  6%|▌         | 129/2117 [04:20<1:02:20,  1.88s/it]
                                                    
{'loss': 1.3772, 'learning_rate': 0.0003997960094239812, 'epoch': 0.06}

  6%|▌         | 129/2117 [04:20<1:02:20,  1.88s/it]
  6%|▌         | 130/2117 [04:22<1:08:02,  2.05s/it]
                                                    
{'loss': 1.5958, 'learning_rate': 0.0003997817011551508, 'epoch': 0.06}

  6%|▌         | 130/2117 [04:22<1:08:02,  2.05s/it]
  6%|▌         | 131/2117 [04:24<1:06:04,  2.00s/it]
                                                    
{'loss': 1.2651, 'learning_rate': 0.00039976690821918917, 'epoch': 0.06}

  6%|▌         | 131/2117 [04:24<1:06:04,  2.00s/it]
  6%|▌         | 132/2117 [04:26<1:03:28,  1.92s/it]
                                                    
{'loss': 1.4289, 'learning_rate': 0.00039975163065198376, 'epoch': 0.06}

  6%|▌         | 132/2117 [04:26<1:03:28,  1.92s/it]
  6%|▋         | 133/2117 [04:28<1:03:56,  1.93s/it]
                                                    
{'loss': 1.3508, 'learning_rate': 0.0003997358684905979, 'epoch': 0.06}

  6%|▋         | 133/2117 [04:28<1:03:56,  1.93s/it]
  6%|▋         | 134/2117 [04:30<1:07:28,  2.04s/it]
                                                    
{'loss': 1.3393, 'learning_rate': 0.00039971962177326996, 'epoch': 0.06}

  6%|▋         | 134/2117 [04:30<1:07:28,  2.04s/it]
  6%|▋         | 135/2117 [04:32<1:09:10,  2.09s/it]
                                                    
{'loss': 1.547, 'learning_rate': 0.0003997028905394146, 'epoch': 0.06}

  6%|▋         | 135/2117 [04:32<1:09:10,  2.09s/it]
  6%|▋         | 136/2117 [04:34<1:07:21,  2.04s/it]
                                                    
{'loss': 1.2613, 'learning_rate': 0.0003996856748296212, 'epoch': 0.06}

  6%|▋         | 136/2117 [04:34<1:07:21,  2.04s/it]
  6%|▋         | 137/2117 [04:36<1:08:34,  2.08s/it]
                                                    
{'loss': 1.6868, 'learning_rate': 0.00039966797468565503, 'epoch': 0.06}

  6%|▋         | 137/2117 [04:36<1:08:34,  2.08s/it]
  7%|▋         | 138/2117 [04:38<1:05:29,  1.99s/it]
                                                    
{'loss': 1.5064, 'learning_rate': 0.00039964979015045626, 'epoch': 0.07}

  7%|▋         | 138/2117 [04:38<1:05:29,  1.99s/it]
  7%|▋         | 139/2117 [04:40<1:05:32,  1.99s/it]
                                                    
{'loss': 1.2082, 'learning_rate': 0.0003996311212681402, 'epoch': 0.07}

  7%|▋         | 139/2117 [04:40<1:05:32,  1.99s/it]
  7%|▋         | 140/2117 [04:42<1:06:06,  2.01s/it]
                                                    
{'loss': 1.4344, 'learning_rate': 0.0003996119680839973, 'epoch': 0.07}

  7%|▋         | 140/2117 [04:42<1:06:06,  2.01s/it]
  7%|▋         | 141/2117 [04:44<1:08:05,  2.07s/it]
                                                    
{'loss': 1.4068, 'learning_rate': 0.000399592330644493, 'epoch': 0.07}

  7%|▋         | 141/2117 [04:44<1:08:05,  2.07s/it]
  7%|▋         | 142/2117 [04:46<1:07:00,  2.04s/it]
                                                    
{'loss': 1.0907, 'learning_rate': 0.0003995722089972672, 'epoch': 0.07}

  7%|▋         | 142/2117 [04:46<1:07:00,  2.04s/it]
  7%|▋         | 143/2117 [04:48<1:04:14,  1.95s/it]
                                                    
{'loss': 1.4394, 'learning_rate': 0.00039955160319113484, 'epoch': 0.07}

  7%|▋         | 143/2117 [04:48<1:04:14,  1.95s/it]
  7%|▋         | 144/2117 [04:50<1:03:10,  1.92s/it]
                                                    
{'loss': 1.2697, 'learning_rate': 0.00039953051327608515, 'epoch': 0.07}

  7%|▋         | 144/2117 [04:50<1:03:10,  1.92s/it]
  7%|▋         | 145/2117 [04:52<1:09:13,  2.11s/it]
                                                    
{'loss': 1.6047, 'learning_rate': 0.00039950893930328204, 'epoch': 0.07}

  7%|▋         | 145/2117 [04:52<1:09:13,  2.11s/it]
  7%|▋         | 146/2117 [04:54<1:07:19,  2.05s/it]
                                                    
{'loss': 1.5132, 'learning_rate': 0.00039948688132506355, 'epoch': 0.07}

  7%|▋         | 146/2117 [04:54<1:07:19,  2.05s/it]
  7%|▋         | 147/2117 [04:57<1:11:22,  2.17s/it]
                                                    
{'loss': 1.6163, 'learning_rate': 0.0003994643393949419, 'epoch': 0.07}

  7%|▋         | 147/2117 [04:57<1:11:22,  2.17s/it]
  7%|▋         | 148/2117 [04:59<1:12:08,  2.20s/it]
                                                    
{'loss': 1.5018, 'learning_rate': 0.0003994413135676036, 'epoch': 0.07}

  7%|▋         | 148/2117 [04:59<1:12:08,  2.20s/it]
  7%|▋         | 149/2117 [05:01<1:09:23,  2.12s/it]
                                                    
{'loss': 1.2945, 'learning_rate': 0.0003994178038989088, 'epoch': 0.07}

  7%|▋         | 149/2117 [05:01<1:09:23,  2.12s/it]
  7%|▋         | 150/2117 [05:03<1:07:10,  2.05s/it]
                                                    
{'loss': 1.7071, 'learning_rate': 0.00039939381044589166, 'epoch': 0.07}

  7%|▋         | 150/2117 [05:03<1:07:10,  2.05s/it]
  7%|▋         | 151/2117 [05:04<1:03:13,  1.93s/it]
                                                    
{'loss': 1.5062, 'learning_rate': 0.0003993693332667598, 'epoch': 0.07}

  7%|▋         | 151/2117 [05:04<1:03:13,  1.93s/it]
  7%|▋         | 152/2117 [05:06<1:01:16,  1.87s/it]
                                                    
{'loss': 1.3906, 'learning_rate': 0.00039934437242089457, 'epoch': 0.07}

  7%|▋         | 152/2117 [05:06<1:01:16,  1.87s/it]
  7%|▋         | 153/2117 [05:08<1:05:14,  1.99s/it]
                                                    
{'loss': 1.2134, 'learning_rate': 0.0003993189279688505, 'epoch': 0.07}

  7%|▋         | 153/2117 [05:08<1:05:14,  1.99s/it]
  7%|▋         | 154/2117 [05:11<1:06:06,  2.02s/it]
                                                    
{'loss': 1.3035, 'learning_rate': 0.0003992929999723554, 'epoch': 0.07}

  7%|▋         | 154/2117 [05:11<1:06:06,  2.02s/it]
  7%|▋         | 155/2117 [05:13<1:07:46,  2.07s/it]
                                                    
{'loss': 1.6205, 'learning_rate': 0.0003992665884943102, 'epoch': 0.07}

  7%|▋         | 155/2117 [05:13<1:07:46,  2.07s/it]
  7%|▋         | 156/2117 [05:15<1:09:17,  2.12s/it]
                                                    
{'loss': 1.6062, 'learning_rate': 0.00039923969359878877, 'epoch': 0.07}

  7%|▋         | 156/2117 [05:15<1:09:17,  2.12s/it]
  7%|▋         | 157/2117 [05:17<1:07:48,  2.08s/it]
                                                    
{'loss': 1.4719, 'learning_rate': 0.00039921231535103757, 'epoch': 0.07}

  7%|▋         | 157/2117 [05:17<1:07:48,  2.08s/it]
  7%|▋         | 158/2117 [05:19<1:08:03,  2.08s/it]
                                                    
{'loss': 1.4253, 'learning_rate': 0.00039918445381747586, 'epoch': 0.07}

  7%|▋         | 158/2117 [05:19<1:08:03,  2.08s/it]
  8%|▊         | 159/2117 [05:21<1:07:58,  2.08s/it]
                                                    
{'loss': 1.4565, 'learning_rate': 0.00039915610906569523, 'epoch': 0.08}

  8%|▊         | 159/2117 [05:21<1:07:58,  2.08s/it]
  8%|▊         | 160/2117 [05:23<1:07:20,  2.06s/it]
                                                    
{'loss': 1.4769, 'learning_rate': 0.0003991272811644596, 'epoch': 0.08}

  8%|▊         | 160/2117 [05:23<1:07:20,  2.06s/it]
  8%|▊         | 161/2117 [05:25<1:06:19,  2.03s/it]
                                                    
{'loss': 1.32, 'learning_rate': 0.00039909797018370497, 'epoch': 0.08}

  8%|▊         | 161/2117 [05:25<1:06:19,  2.03s/it]
  8%|▊         | 162/2117 [05:27<1:05:16,  2.00s/it]
                                                    
{'loss': 1.3666, 'learning_rate': 0.00039906817619453935, 'epoch': 0.08}

  8%|▊         | 162/2117 [05:27<1:05:16,  2.00s/it]
  8%|▊         | 163/2117 [05:29<1:03:15,  1.94s/it]
                                                    
{'loss': 1.4335, 'learning_rate': 0.0003990378992692424, 'epoch': 0.08}

  8%|▊         | 163/2117 [05:29<1:03:15,  1.94s/it]
  8%|▊         | 164/2117 [05:31<1:03:39,  1.96s/it]
                                                    
{'loss': 1.3242, 'learning_rate': 0.0003990071394812654, 'epoch': 0.08}

  8%|▊         | 164/2117 [05:31<1:03:39,  1.96s/it]
  8%|▊         | 165/2117 [05:33<1:03:16,  1.95s/it]
                                                    
{'loss': 1.3204, 'learning_rate': 0.00039897589690523127, 'epoch': 0.08}

  8%|▊         | 165/2117 [05:33<1:03:16,  1.95s/it]
  8%|▊         | 166/2117 [05:35<1:03:36,  1.96s/it]
                                                    
{'loss': 1.3507, 'learning_rate': 0.00039894417161693386, 'epoch': 0.08}

  8%|▊         | 166/2117 [05:35<1:03:36,  1.96s/it]
  8%|▊         | 167/2117 [05:37<1:05:13,  2.01s/it]
                                                    
{'loss': 1.6852, 'learning_rate': 0.00039891196369333813, 'epoch': 0.08}

  8%|▊         | 167/2117 [05:37<1:05:13,  2.01s/it]
  8%|▊         | 168/2117 [05:39<1:04:39,  1.99s/it]
                                                    
{'loss': 1.5667, 'learning_rate': 0.0003988792732125801, 'epoch': 0.08}

  8%|▊         | 168/2117 [05:39<1:04:39,  1.99s/it]
  8%|▊         | 169/2117 [05:41<1:05:50,  2.03s/it]
                                                    
{'loss': 1.4404, 'learning_rate': 0.0003988461002539664, 'epoch': 0.08}

  8%|▊         | 169/2117 [05:41<1:05:50,  2.03s/it]
  8%|▊         | 170/2117 [05:43<1:05:11,  2.01s/it]
                                                    
{'loss': 1.4452, 'learning_rate': 0.0003988124448979739, 'epoch': 0.08}

  8%|▊         | 170/2117 [05:43<1:05:11,  2.01s/it]
  8%|▊         | 171/2117 [05:45<1:04:22,  1.98s/it]
                                                    
{'loss': 1.6917, 'learning_rate': 0.00039877830722625, 'epoch': 0.08}

  8%|▊         | 171/2117 [05:45<1:04:22,  1.98s/it]
  8%|▊         | 172/2117 [05:47<1:04:44,  2.00s/it]
                                                    
{'loss': 1.7008, 'learning_rate': 0.0003987436873216122, 'epoch': 0.08}

  8%|▊         | 172/2117 [05:47<1:04:44,  2.00s/it]
  8%|▊         | 173/2117 [05:49<1:04:19,  1.99s/it]
                                                    
{'loss': 1.5154, 'learning_rate': 0.0003987085852680478, 'epoch': 0.08}

  8%|▊         | 173/2117 [05:49<1:04:19,  1.99s/it]
  8%|▊         | 174/2117 [05:51<1:01:53,  1.91s/it]
                                                    
{'loss': 1.3697, 'learning_rate': 0.00039867300115071374, 'epoch': 0.08}

  8%|▊         | 174/2117 [05:51<1:01:53,  1.91s/it]
  8%|▊         | 175/2117 [05:53<1:02:45,  1.94s/it]
                                                    
{'loss': 1.3099, 'learning_rate': 0.00039863693505593655, 'epoch': 0.08}

  8%|▊         | 175/2117 [05:53<1:02:45,  1.94s/it]
  8%|▊         | 176/2117 [05:55<1:05:31,  2.03s/it]
                                                    
{'loss': 1.3247, 'learning_rate': 0.00039860038707121204, 'epoch': 0.08}

  8%|▊         | 176/2117 [05:55<1:05:31,  2.03s/it]
  8%|▊         | 177/2117 [05:57<1:03:45,  1.97s/it]
                                                    
{'loss': 1.4999, 'learning_rate': 0.00039856335728520495, 'epoch': 0.08}

  8%|▊         | 177/2117 [05:57<1:03:45,  1.97s/it]
  8%|▊         | 178/2117 [05:59<1:04:17,  1.99s/it]
                                                    
{'loss': 1.4268, 'learning_rate': 0.0003985258457877489, 'epoch': 0.08}

  8%|▊         | 178/2117 [05:59<1:04:17,  1.99s/it]
  8%|▊         | 179/2117 [06:01<1:04:44,  2.00s/it]
                                                    
{'loss': 1.7714, 'learning_rate': 0.0003984878526698463, 'epoch': 0.08}

  8%|▊         | 179/2117 [06:01<1:04:44,  2.00s/it]
  9%|▊         | 180/2117 [06:03<1:03:12,  1.96s/it]
                                                    
{'loss': 1.356, 'learning_rate': 0.0003984493780236677, 'epoch': 0.08}

  9%|▊         | 180/2117 [06:03<1:03:12,  1.96s/it]
  9%|▊         | 181/2117 [06:04<1:02:43,  1.94s/it]
                                                    
{'loss': 1.2773, 'learning_rate': 0.00039841042194255205, 'epoch': 0.09}

  9%|▊         | 181/2117 [06:04<1:02:43,  1.94s/it]
  9%|▊         | 182/2117 [06:06<1:00:04,  1.86s/it]
                                                    
{'loss': 1.0558, 'learning_rate': 0.0003983709845210061, 'epoch': 0.09}

  9%|▊         | 182/2117 [06:06<1:00:04,  1.86s/it]
  9%|▊         | 183/2117 [06:08<59:41,  1.85s/it]  
                                                  
{'loss': 1.7372, 'learning_rate': 0.0003983310658547044, 'epoch': 0.09}

  9%|▊         | 183/2117 [06:08<59:41,  1.85s/it]
  9%|▊         | 184/2117 [06:10<58:59,  1.83s/it]
                                                  
{'loss': 1.5983, 'learning_rate': 0.000398290666040489, 'epoch': 0.09}

  9%|▊         | 184/2117 [06:10<58:59,  1.83s/it]
  9%|▊         | 185/2117 [06:12<1:00:24,  1.88s/it]
                                                    
{'loss': 1.437, 'learning_rate': 0.0003982497851763692, 'epoch': 0.09}

  9%|▊         | 185/2117 [06:12<1:00:24,  1.88s/it]
  9%|▉         | 186/2117 [06:14<1:01:11,  1.90s/it]
                                                    
{'loss': 1.3883, 'learning_rate': 0.0003982084233615213, 'epoch': 0.09}

  9%|▉         | 186/2117 [06:14<1:01:11,  1.90s/it]
  9%|▉         | 187/2117 [06:16<1:02:34,  1.95s/it]
                                                    
{'loss': 1.5226, 'learning_rate': 0.00039816658069628835, 'epoch': 0.09}

  9%|▉         | 187/2117 [06:16<1:02:34,  1.95s/it]
  9%|▉         | 188/2117 [06:18<1:01:00,  1.90s/it]
                                                    
{'loss': 1.4093, 'learning_rate': 0.00039812425728218, 'epoch': 0.09}

  9%|▉         | 188/2117 [06:18<1:01:00,  1.90s/it]
  9%|▉         | 189/2117 [06:19<1:00:49,  1.89s/it]
                                                    
{'loss': 1.3972, 'learning_rate': 0.00039808145322187214, 'epoch': 0.09}

  9%|▉         | 189/2117 [06:19<1:00:49,  1.89s/it]
  9%|▉         | 190/2117 [06:22<1:03:00,  1.96s/it]
                                                    
{'loss': 1.1755, 'learning_rate': 0.00039803816861920673, 'epoch': 0.09}

  9%|▉         | 190/2117 [06:22<1:03:00,  1.96s/it]
  9%|▉         | 191/2117 [06:24<1:03:42,  1.98s/it]
                                                    
{'loss': 1.606, 'learning_rate': 0.00039799440357919153, 'epoch': 0.09}

  9%|▉         | 191/2117 [06:24<1:03:42,  1.98s/it]
  9%|▉         | 192/2117 [06:26<1:04:11,  2.00s/it]
                                                    
{'loss': 1.5801, 'learning_rate': 0.0003979501582079997, 'epoch': 0.09}

  9%|▉         | 192/2117 [06:26<1:04:11,  2.00s/it]
  9%|▉         | 193/2117 [06:28<1:04:18,  2.01s/it]
                                                    
{'loss': 1.4668, 'learning_rate': 0.00039790543261296996, 'epoch': 0.09}

  9%|▉         | 193/2117 [06:28<1:04:18,  2.01s/it]
  9%|▉         | 194/2117 [06:30<1:03:53,  1.99s/it]
                                                    
{'loss': 1.5086, 'learning_rate': 0.0003978602269026057, 'epoch': 0.09}

  9%|▉         | 194/2117 [06:30<1:03:53,  1.99s/it]
  9%|▉         | 195/2117 [06:31<1:02:03,  1.94s/it]
                                                    
{'loss': 1.3564, 'learning_rate': 0.0003978145411865754, 'epoch': 0.09}

  9%|▉         | 195/2117 [06:31<1:02:03,  1.94s/it]
  9%|▉         | 196/2117 [06:34<1:03:56,  2.00s/it]
                                                    
{'loss': 1.53, 'learning_rate': 0.00039776837557571176, 'epoch': 0.09}

  9%|▉         | 196/2117 [06:34<1:03:56,  2.00s/it]
  9%|▉         | 197/2117 [06:36<1:06:10,  2.07s/it]
                                                    
{'loss': 1.5908, 'learning_rate': 0.0003977217301820117, 'epoch': 0.09}

  9%|▉         | 197/2117 [06:36<1:06:10,  2.07s/it]
  9%|▉         | 198/2117 [06:37<1:02:48,  1.96s/it]
                                                    
{'loss': 1.1649, 'learning_rate': 0.0003976746051186364, 'epoch': 0.09}

  9%|▉         | 198/2117 [06:37<1:02:48,  1.96s/it]
  9%|▉         | 199/2117 [06:39<1:00:42,  1.90s/it]
                                                    
{'loss': 1.4091, 'learning_rate': 0.0003976270004999103, 'epoch': 0.09}

  9%|▉         | 199/2117 [06:39<1:00:42,  1.90s/it]
  9%|▉         | 200/2117 [06:41<1:01:01,  1.91s/it]
                                                    
{'loss': 1.5316, 'learning_rate': 0.00039757891644132153, 'epoch': 0.09}

  9%|▉         | 200/2117 [06:41<1:01:01,  1.91s/it]
  9%|▉         | 201/2117 [06:43<58:57,  1.85s/it]  
                                                  
{'loss': 1.5858, 'learning_rate': 0.0003975303530595212, 'epoch': 0.09}

  9%|▉         | 201/2117 [06:43<58:57,  1.85s/it]
 10%|▉         | 202/2117 [06:45<1:00:10,  1.89s/it]
                                                    
{'loss': 1.6776, 'learning_rate': 0.0003974813104723233, 'epoch': 0.1}

 10%|▉         | 202/2117 [06:45<1:00:10,  1.89s/it]
 10%|▉         | 203/2117 [06:47<1:01:12,  1.92s/it]
                                                    
{'loss': 1.4326, 'learning_rate': 0.00039743178879870426, 'epoch': 0.1}

 10%|▉         | 203/2117 [06:47<1:01:12,  1.92s/it]
 10%|▉         | 204/2117 [06:49<1:03:53,  2.00s/it]
                                                    
{'loss': 1.2465, 'learning_rate': 0.00039738178815880296, 'epoch': 0.1}

 10%|▉         | 204/2117 [06:49<1:03:53,  2.00s/it]
 10%|▉         | 205/2117 [06:51<1:04:20,  2.02s/it]
                                                    
{'loss': 1.374, 'learning_rate': 0.00039733130867392004, 'epoch': 0.1}

 10%|▉         | 205/2117 [06:51<1:04:20,  2.02s/it]
 10%|▉         | 206/2117 [06:53<1:03:43,  2.00s/it]
                                                    
{'loss': 1.5635, 'learning_rate': 0.00039728035046651797, 'epoch': 0.1}

 10%|▉         | 206/2117 [06:53<1:03:43,  2.00s/it]
 10%|▉         | 207/2117 [06:55<1:03:44,  2.00s/it]
                                                    
{'loss': 1.3614, 'learning_rate': 0.0003972289136602204, 'epoch': 0.1}

 10%|▉         | 207/2117 [06:55<1:03:44,  2.00s/it]
 10%|▉         | 208/2117 [06:57<1:06:42,  2.10s/it]
                                                    
{'loss': 1.2695, 'learning_rate': 0.0003971769983798124, 'epoch': 0.1}

 10%|▉         | 208/2117 [06:57<1:06:42,  2.10s/it]
 10%|▉         | 209/2117 [06:59<1:03:46,  2.01s/it]
                                                    
{'loss': 1.4433, 'learning_rate': 0.00039712460475123943, 'epoch': 0.1}

 10%|▉         | 209/2117 [06:59<1:03:46,  2.01s/it]
 10%|▉         | 210/2117 [07:01<1:06:01,  2.08s/it]
                                                    
{'loss': 1.4809, 'learning_rate': 0.00039707173290160766, 'epoch': 0.1}

 10%|▉         | 210/2117 [07:01<1:06:01,  2.08s/it]
 10%|▉         | 211/2117 [07:03<1:06:06,  2.08s/it]
                                                    
{'loss': 1.3501, 'learning_rate': 0.00039701838295918315, 'epoch': 0.1}

 10%|▉         | 211/2117 [07:04<1:06:06,  2.08s/it]
 10%|█         | 212/2117 [07:05<1:04:21,  2.03s/it]
                                                    
{'loss': 1.3736, 'learning_rate': 0.0003969645550533922, 'epoch': 0.1}

 10%|█         | 212/2117 [07:05<1:04:21,  2.03s/it]
 10%|█         | 213/2117 [07:07<1:03:32,  2.00s/it]
                                                    
{'loss': 1.1709, 'learning_rate': 0.0003969102493148203, 'epoch': 0.1}

 10%|█         | 213/2117 [07:07<1:03:32,  2.00s/it]
 10%|█         | 214/2117 [07:09<1:02:21,  1.97s/it]
                                                    
{'loss': 1.3236, 'learning_rate': 0.00039685546587521237, 'epoch': 0.1}

 10%|█         | 214/2117 [07:09<1:02:21,  1.97s/it]
 10%|█         | 215/2117 [07:11<1:01:33,  1.94s/it]
                                                    
{'loss': 1.2384, 'learning_rate': 0.00039680020486747205, 'epoch': 0.1}

 10%|█         | 215/2117 [07:11<1:01:33,  1.94s/it]
 10%|█         | 216/2117 [07:13<1:01:12,  1.93s/it]
                                                    
{'loss': 1.3349, 'learning_rate': 0.00039674446642566175, 'epoch': 0.1}

 10%|█         | 216/2117 [07:13<1:01:12,  1.93s/it]
 10%|█         | 217/2117 [07:15<1:05:02,  2.05s/it]
                                                    
{'loss': 1.3416, 'learning_rate': 0.0003966882506850018, 'epoch': 0.1}

 10%|█         | 217/2117 [07:15<1:05:02,  2.05s/it]
 10%|█         | 218/2117 [07:17<1:01:56,  1.96s/it]
                                                    
{'loss': 1.132, 'learning_rate': 0.0003966315577818709, 'epoch': 0.1}

 10%|█         | 218/2117 [07:17<1:01:56,  1.96s/it]
 10%|█         | 219/2117 [07:19<1:03:04,  1.99s/it]
                                                    
{'loss': 1.4271, 'learning_rate': 0.000396574387853805, 'epoch': 0.1}

 10%|█         | 219/2117 [07:19<1:03:04,  1.99s/it]
 10%|█         | 220/2117 [07:21<1:01:25,  1.94s/it]
                                                    
{'loss': 1.5336, 'learning_rate': 0.0003965167410394975, 'epoch': 0.1}

 10%|█         | 220/2117 [07:21<1:01:25,  1.94s/it]
 10%|█         | 221/2117 [07:23<1:02:50,  1.99s/it]
                                                    
{'loss': 1.1742, 'learning_rate': 0.00039645861747879843, 'epoch': 0.1}

 10%|█         | 221/2117 [07:23<1:02:50,  1.99s/it]
 10%|█         | 222/2117 [07:25<1:01:58,  1.96s/it]
                                                    
{'loss': 1.2881, 'learning_rate': 0.0003964000173127148, 'epoch': 0.1}

 10%|█         | 222/2117 [07:25<1:01:58,  1.96s/it]
 11%|█         | 223/2117 [07:27<1:03:22,  2.01s/it]
                                                    
{'loss': 1.3789, 'learning_rate': 0.0003963409406834095, 'epoch': 0.11}

 11%|█         | 223/2117 [07:27<1:03:22,  2.01s/it]
 11%|█         | 224/2117 [07:30<1:07:24,  2.14s/it]
                                                    
{'loss': 1.5167, 'learning_rate': 0.00039628138773420147, 'epoch': 0.11}

 11%|█         | 224/2117 [07:30<1:07:24,  2.14s/it]
 11%|█         | 225/2117 [07:32<1:08:57,  2.19s/it]
                                                    
{'loss': 1.4953, 'learning_rate': 0.0003962213586095653, 'epoch': 0.11}

 11%|█         | 225/2117 [07:32<1:08:57,  2.19s/it]
 11%|█         | 226/2117 [07:34<1:06:54,  2.12s/it]
                                                    
{'loss': 1.7612, 'learning_rate': 0.0003961608534551307, 'epoch': 0.11}

 11%|█         | 226/2117 [07:34<1:06:54,  2.12s/it]
 11%|█         | 227/2117 [07:36<1:04:49,  2.06s/it]
                                                    
{'loss': 1.2886, 'learning_rate': 0.000396099872417682, 'epoch': 0.11}

 11%|█         | 227/2117 [07:36<1:04:49,  2.06s/it]
 11%|█         | 228/2117 [07:38<1:03:06,  2.00s/it]
                                                    
{'loss': 1.3367, 'learning_rate': 0.0003960384156451583, 'epoch': 0.11}

 11%|█         | 228/2117 [07:38<1:03:06,  2.00s/it]
 11%|█         | 229/2117 [07:40<1:02:44,  1.99s/it]
                                                    
{'loss': 1.6526, 'learning_rate': 0.0003959764832866527, 'epoch': 0.11}

 11%|█         | 229/2117 [07:40<1:02:44,  1.99s/it]
 11%|█         | 230/2117 [07:41<1:00:52,  1.94s/it]
                                                    
{'loss': 1.3393, 'learning_rate': 0.0003959140754924121, 'epoch': 0.11}

 11%|█         | 230/2117 [07:41<1:00:52,  1.94s/it]
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{'loss': 1.6188, 'learning_rate': 0.0003958511924138368, 'epoch': 0.11}

 11%|█         | 231/2117 [07:44<1:05:48,  2.09s/it]
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{'loss': 1.163, 'learning_rate': 0.0003957878342034801, 'epoch': 0.11}

 11%|█         | 232/2117 [07:46<1:03:19,  2.02s/it]
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{'loss': 0.9372, 'learning_rate': 0.00039572400101504793, 'epoch': 0.11}

 11%|█         | 233/2117 [07:47<1:00:13,  1.92s/it]
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{'loss': 1.3556, 'learning_rate': 0.00039565969300339867, 'epoch': 0.11}

 11%|█         | 234/2117 [07:49<59:54,  1.91s/it]
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{'loss': 1.3058, 'learning_rate': 0.0003955949103245424, 'epoch': 0.11}

 11%|█         | 235/2117 [07:51<58:30,  1.87s/it]
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{'loss': 1.3413, 'learning_rate': 0.00039552965313564083, 'epoch': 0.11}

 11%|█         | 236/2117 [07:53<59:09,  1.89s/it]
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{'loss': 1.6436, 'learning_rate': 0.000395463921595007, 'epoch': 0.11}

 11%|█         | 237/2117 [07:55<1:01:48,  1.97s/it]
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{'loss': 1.3746, 'learning_rate': 0.0003953977158621043, 'epoch': 0.11}

 11%|█         | 238/2117 [07:57<1:01:07,  1.95s/it]
 11%|█▏        | 239/2117 [07:59<1:01:49,  1.98s/it]
                                                    
{'loss': 1.4178, 'learning_rate': 0.00039533103609754695, 'epoch': 0.11}

 11%|█▏        | 239/2117 [07:59<1:01:49,  1.98s/it]
 11%|█▏        | 240/2117 [08:01<1:02:06,  1.99s/it]
                                                    
{'loss': 1.5444, 'learning_rate': 0.0003952638824630989, 'epoch': 0.11}

 11%|█▏        | 240/2117 [08:01<1:02:06,  1.99s/it]
 11%|█▏        | 241/2117 [08:03<1:03:07,  2.02s/it]
                                                    
{'loss': 1.6739, 'learning_rate': 0.00039519625512167373, 'epoch': 0.11}

 11%|█▏        | 241/2117 [08:03<1:03:07,  2.02s/it]
 11%|█▏        | 242/2117 [08:05<1:01:25,  1.97s/it]
                                                    
{'loss': 1.3911, 'learning_rate': 0.0003951281542373343, 'epoch': 0.11}

 11%|█▏        | 242/2117 [08:05<1:01:25,  1.97s/it]
 11%|█▏        | 243/2117 [08:07<1:01:04,  1.96s/it]
                                                    
{'loss': 1.2932, 'learning_rate': 0.0003950595799752922, 'epoch': 0.11}

 11%|█▏        | 243/2117 [08:07<1:01:04,  1.96s/it]
 12%|█▏        | 244/2117 [08:09<1:02:26,  2.00s/it]
                                                    
{'loss': 1.5476, 'learning_rate': 0.00039499053250190754, 'epoch': 0.12}

 12%|█▏        | 244/2117 [08:09<1:02:26,  2.00s/it]
 12%|█▏        | 245/2117 [08:11<1:01:56,  1.99s/it]
                                                    
{'loss': 1.5088, 'learning_rate': 0.00039492101198468826, 'epoch': 0.12}

 12%|█▏        | 245/2117 [08:11<1:01:56,  1.99s/it]
 12%|█▏        | 246/2117 [08:13<1:02:30,  2.00s/it]
                                                    
{'loss': 1.4024, 'learning_rate': 0.0003948510185922901, 'epoch': 0.12}

 12%|█▏        | 246/2117 [08:13<1:02:30,  2.00s/it]
 12%|█▏        | 247/2117 [08:15<1:03:18,  2.03s/it]
                                                    
{'loss': 1.4, 'learning_rate': 0.00039478055249451575, 'epoch': 0.12}

 12%|█▏        | 247/2117 [08:15<1:03:18,  2.03s/it]
 12%|█▏        | 248/2117 [08:17<1:01:05,  1.96s/it]
                                                    
{'loss': 1.2615, 'learning_rate': 0.00039470961386231496, 'epoch': 0.12}

 12%|█▏        | 248/2117 [08:17<1:01:05,  1.96s/it]
 12%|█▏        | 249/2117 [08:19<1:01:10,  1.96s/it]
                                                    
{'loss': 1.4472, 'learning_rate': 0.0003946382028677836, 'epoch': 0.12}

 12%|█▏        | 249/2117 [08:19<1:01:10,  1.96s/it]
 12%|█▏        | 250/2117 [08:21<1:02:45,  2.02s/it]
                                                    
{'loss': 1.4282, 'learning_rate': 0.00039456631968416364, 'epoch': 0.12}

 12%|█▏        | 250/2117 [08:21<1:02:45,  2.02s/it]
 12%|█▏        | 251/2117 [08:23<1:02:10,  2.00s/it]
                                                    
{'loss': 1.2253, 'learning_rate': 0.00039449396448584237, 'epoch': 0.12}

 12%|█▏        | 251/2117 [08:23<1:02:10,  2.00s/it]
 12%|█▏        | 252/2117 [08:25<1:01:25,  1.98s/it]
                                                    
{'loss': 1.6736, 'learning_rate': 0.0003944211374483524, 'epoch': 0.12}

 12%|█▏        | 252/2117 [08:25<1:01:25,  1.98s/it]
 12%|█▏        | 253/2117 [08:27<1:05:43,  2.12s/it]
                                                    
{'loss': 1.5025, 'learning_rate': 0.00039434783874837097, 'epoch': 0.12}

 12%|█▏        | 253/2117 [08:27<1:05:43,  2.12s/it]
 12%|█▏        | 254/2117 [08:29<1:04:06,  2.06s/it]
                                                    
{'loss': 1.5902, 'learning_rate': 0.00039427406856371944, 'epoch': 0.12}

 12%|█▏        | 254/2117 [08:29<1:04:06,  2.06s/it]
 12%|█▏        | 255/2117 [08:31<1:01:07,  1.97s/it]
                                                    
{'loss': 1.1451, 'learning_rate': 0.0003941998270733631, 'epoch': 0.12}

 12%|█▏        | 255/2117 [08:31<1:01:07,  1.97s/it]
 12%|█▏        | 256/2117 [08:33<1:01:08,  1.97s/it]
                                                    
{'loss': 1.5581, 'learning_rate': 0.00039412511445741067, 'epoch': 0.12}

 12%|█▏        | 256/2117 [08:33<1:01:08,  1.97s/it]
 12%|█▏        | 257/2117 [08:35<1:02:36,  2.02s/it]
                                                    
{'loss': 1.5489, 'learning_rate': 0.0003940499308971136, 'epoch': 0.12}

 12%|█▏        | 257/2117 [08:35<1:02:36,  2.02s/it]
 12%|█▏        | 258/2117 [08:37<1:01:15,  1.98s/it]
                                                    
{'loss': 1.5395, 'learning_rate': 0.00039397427657486604, 'epoch': 0.12}

 12%|█▏        | 258/2117 [08:37<1:01:15,  1.98s/it]
 12%|█▏        | 259/2117 [08:39<1:00:18,  1.95s/it]
                                                    
{'loss': 1.5695, 'learning_rate': 0.0003938981516742041, 'epoch': 0.12}

 12%|█▏        | 259/2117 [08:39<1:00:18,  1.95s/it]
 12%|█▏        | 260/2117 [08:41<58:03,  1.88s/it]  
                                                  
{'loss': 1.4453, 'learning_rate': 0.0003938215563798057, 'epoch': 0.12}

 12%|█▏        | 260/2117 [08:41<58:03,  1.88s/it]
 12%|█▏        | 261/2117 [08:43<1:00:31,  1.96s/it]
                                                    
{'loss': 1.505, 'learning_rate': 0.0003937444908774895, 'epoch': 0.12}

 12%|█▏        | 261/2117 [08:43<1:00:31,  1.96s/it]
 12%|█▏        | 262/2117 [08:45<1:02:55,  2.04s/it]
                                                    
{'loss': 1.4602, 'learning_rate': 0.0003936669553542153, 'epoch': 0.12}

 12%|█▏        | 262/2117 [08:45<1:02:55,  2.04s/it]
 12%|█▏        | 263/2117 [08:47<1:03:52,  2.07s/it]
                                                    
{'loss': 1.4522, 'learning_rate': 0.0003935889499980831, 'epoch': 0.12}

 12%|█▏        | 263/2117 [08:47<1:03:52,  2.07s/it]
 12%|█▏        | 264/2117 [08:49<1:03:43,  2.06s/it]
                                                    
{'loss': 1.4439, 'learning_rate': 0.00039351047499833235, 'epoch': 0.12}

 12%|█▏        | 264/2117 [08:49<1:03:43,  2.06s/it]
 13%|█▎        | 265/2117 [08:51<1:01:49,  2.00s/it]
                                                    
{'loss': 1.6039, 'learning_rate': 0.0003934315305453423, 'epoch': 0.13}

 13%|█▎        | 265/2117 [08:51<1:01:49,  2.00s/it]
 13%|█▎        | 266/2117 [08:53<59:42,  1.94s/it]  
                                                  
{'loss': 1.2475, 'learning_rate': 0.0003933521168306309, 'epoch': 0.13}

 13%|█▎        | 266/2117 [08:53<59:42,  1.94s/it]
 13%|█▎        | 267/2117 [08:55<1:00:48,  1.97s/it]
                                                    
{'loss': 1.4151, 'learning_rate': 0.0003932722340468545, 'epoch': 0.13}

 13%|█▎        | 267/2117 [08:55<1:00:48,  1.97s/it]
 13%|█▎        | 268/2117 [08:57<59:19,  1.93s/it]  
                                                  
{'loss': 1.3802, 'learning_rate': 0.0003931918823878074, 'epoch': 0.13}

 13%|█▎        | 268/2117 [08:57<59:19,  1.93s/it]
 13%|█▎        | 269/2117 [08:59<1:00:20,  1.96s/it]
                                                    
{'loss': 1.4119, 'learning_rate': 0.0003931110620484215, 'epoch': 0.13}

 13%|█▎        | 269/2117 [08:59<1:00:20,  1.96s/it]
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{'loss': 1.2728, 'learning_rate': 0.0003930297732247654, 'epoch': 0.13}

 13%|█▎        | 270/2117 [09:01<59:31,  1.93s/it]
 13%|█▎        | 271/2117 [09:03<59:29,  1.93s/it]
                                                  
{'loss': 1.4114, 'learning_rate': 0.0003929480161140446, 'epoch': 0.13}

 13%|█▎        | 271/2117 [09:03<59:29,  1.93s/it]
 13%|█▎        | 272/2117 [09:05<1:00:38,  1.97s/it]
                                                    
{'loss': 1.4245, 'learning_rate': 0.0003928657909146006, 'epoch': 0.13}

 13%|█▎        | 272/2117 [09:05<1:00:38,  1.97s/it]
 13%|█▎        | 273/2117 [09:06<58:00,  1.89s/it]  
                                                  
{'loss': 1.5001, 'learning_rate': 0.00039278309782591014, 'epoch': 0.13}

 13%|█▎        | 273/2117 [09:06<58:00,  1.89s/it]
 13%|█▎        | 274/2117 [09:08<57:02,  1.86s/it]
                                                  
{'loss': 1.2046, 'learning_rate': 0.0003926999370485856, 'epoch': 0.13}

 13%|█▎        | 274/2117 [09:08<57:02,  1.86s/it]
 13%|█▎        | 275/2117 [09:10<57:20,  1.87s/it]
                                                  
{'loss': 1.409, 'learning_rate': 0.00039261630878437343, 'epoch': 0.13}

 13%|█▎        | 275/2117 [09:10<57:20,  1.87s/it]
 13%|█▎        | 276/2117 [09:12<1:01:49,  2.01s/it]
                                                    
{'loss': 1.4574, 'learning_rate': 0.00039253221323615444, 'epoch': 0.13}

 13%|█▎        | 276/2117 [09:12<1:01:49,  2.01s/it]
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{'loss': 1.5181, 'learning_rate': 0.00039244765060794315, 'epoch': 0.13}

 13%|█▎        | 277/2117 [09:14<1:01:29,  2.01s/it]
 13%|█▎        | 278/2117 [09:16<1:01:41,  2.01s/it]
                                                    
{'loss': 1.2098, 'learning_rate': 0.0003923626211048871, 'epoch': 0.13}

 13%|█▎        | 278/2117 [09:16<1:01:41,  2.01s/it]
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{'loss': 1.5624, 'learning_rate': 0.00039227712493326644, 'epoch': 0.13}

 13%|█▎        | 279/2117 [09:18<1:02:38,  2.04s/it]
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{'loss': 1.5203, 'learning_rate': 0.0003921911623004935, 'epoch': 0.13}

 13%|█▎        | 280/2117 [09:21<1:03:03,  2.06s/it]
 13%|█▎        | 281/2117 [09:22<1:01:17,  2.00s/it]
                                                    
{'loss': 1.2721, 'learning_rate': 0.0003921047334151121, 'epoch': 0.13}

 13%|█▎        | 281/2117 [09:22<1:01:17,  2.00s/it]
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{'loss': 1.2674, 'learning_rate': 0.0003920178384867975, 'epoch': 0.13}

 13%|█▎        | 282/2117 [09:25<1:03:18,  2.07s/it]
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{'loss': 1.574, 'learning_rate': 0.0003919304777263553, 'epoch': 0.13}

 13%|█▎        | 283/2117 [09:27<1:03:23,  2.07s/it]
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{'loss': 1.5655, 'learning_rate': 0.0003918426513457211, 'epoch': 0.13}

 13%|█▎        | 284/2117 [09:29<1:01:46,  2.02s/it]
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{'loss': 1.4375, 'learning_rate': 0.0003917543595579605, 'epoch': 0.13}

 13%|█▎        | 285/2117 [09:31<1:02:13,  2.04s/it]
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{'loss': 1.4467, 'learning_rate': 0.0003916656025772678, 'epoch': 0.14}

 14%|█▎        | 286/2117 [09:32<59:20,  1.94s/it]
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{'loss': 1.1648, 'learning_rate': 0.000391576380618966, 'epoch': 0.14}

 14%|█▎        | 287/2117 [09:34<57:28,  1.88s/it]
 14%|█▎        | 288/2117 [09:36<56:34,  1.86s/it]
                                                  
{'loss': 1.4204, 'learning_rate': 0.0003914866938995062, 'epoch': 0.14}

 14%|█▎        | 288/2117 [09:36<56:34,  1.86s/it]
 14%|█▎        | 289/2117 [09:38<1:01:36,  2.02s/it]
                                                    
{'loss': 1.0229, 'learning_rate': 0.00039139654263646676, 'epoch': 0.14}

 14%|█▎        | 289/2117 [09:38<1:01:36,  2.02s/it]
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{'loss': 1.3691, 'learning_rate': 0.00039130592704855326, 'epoch': 0.14}

 14%|█▎        | 290/2117 [09:40<1:01:20,  2.01s/it]
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{'loss': 1.4937, 'learning_rate': 0.00039121484735559755, 'epoch': 0.14}

 14%|█▎        | 291/2117 [09:43<1:04:02,  2.10s/it]
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{'loss': 1.2192, 'learning_rate': 0.0003911233037785576, 'epoch': 0.14}

 14%|█▍        | 292/2117 [09:45<1:02:11,  2.04s/it]
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{'loss': 1.1896, 'learning_rate': 0.00039103129653951654, 'epoch': 0.14}

 14%|█▍        | 293/2117 [09:47<1:06:12,  2.18s/it]
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{'loss': 1.1806, 'learning_rate': 0.0003909388258616824, 'epoch': 0.14}

 14%|█▍        | 294/2117 [09:49<1:04:32,  2.12s/it]
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{'loss': 1.4472, 'learning_rate': 0.00039084589196938753, 'epoch': 0.14}

 14%|█▍        | 295/2117 [09:51<1:03:17,  2.08s/it]
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{'loss': 1.2835, 'learning_rate': 0.0003907524950880881, 'epoch': 0.14}

 14%|█▍        | 296/2117 [09:53<1:00:28,  1.99s/it]
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{'loss': 1.6074, 'learning_rate': 0.00039065863544436347, 'epoch': 0.14}

 14%|█▍        | 297/2117 [09:55<1:01:10,  2.02s/it]
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{'loss': 1.6836, 'learning_rate': 0.0003905643132659154, 'epoch': 0.14}

 14%|█▍        | 298/2117 [09:57<1:01:32,  2.03s/it]
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{'loss': 1.1548, 'learning_rate': 0.0003904695287815681, 'epoch': 0.14}

 14%|█▍        | 299/2117 [09:59<1:00:59,  2.01s/it]
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{'loss': 1.3484, 'learning_rate': 0.0003903742822212671, 'epoch': 0.14}

 14%|█▍        | 300/2117 [10:01<1:01:39,  2.04s/it]
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{'loss': 1.568, 'learning_rate': 0.000390278573816079, 'epoch': 0.14}

 14%|█▍        | 301/2117 [10:03<59:41,  1.97s/it]
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{'loss': 1.4065, 'learning_rate': 0.0003901824037981909, 'epoch': 0.14}

 14%|█▍        | 302/2117 [10:05<1:00:55,  2.01s/it]
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{'loss': 1.4226, 'learning_rate': 0.0003900857724009096, 'epoch': 0.14}

 14%|█▍        | 303/2117 [10:07<1:00:52,  2.01s/it]
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{'loss': 1.4137, 'learning_rate': 0.0003899886798586613, 'epoch': 0.14}

 14%|█▍        | 304/2117 [10:09<1:00:25,  2.00s/it]
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{'loss': 1.2969, 'learning_rate': 0.00038989112640699095, 'epoch': 0.14}

 14%|█▍        | 305/2117 [10:11<1:04:48,  2.15s/it]
 14%|█▍        | 306/2117 [10:13<1:03:07,  2.09s/it]
                                                    
{'loss': 1.4033, 'learning_rate': 0.0003897931122825616, 'epoch': 0.14}

 14%|█▍        | 306/2117 [10:13<1:03:07,  2.09s/it]
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{'loss': 1.5774, 'learning_rate': 0.0003896946377231539, 'epoch': 0.14}

 15%|█▍        | 307/2117 [10:15<1:01:57,  2.05s/it]
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{'loss': 1.4064, 'learning_rate': 0.00038959570296766554, 'epoch': 0.15}

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{'loss': 1.6194, 'learning_rate': 0.0003894963082561106, 'epoch': 0.15}

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{'loss': 1.3165, 'learning_rate': 0.000389396453829619, 'epoch': 0.15}

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{'loss': 1.5399, 'learning_rate': 0.000389296139930436, 'epoch': 0.15}

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{'loss': 1.3126, 'learning_rate': 0.0003891953668019214, 'epoch': 0.15}

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{'loss': 1.4554, 'learning_rate': 0.00038909413468854925, 'epoch': 0.15}

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{'loss': 1.1586, 'learning_rate': 0.0003889924438359069, 'epoch': 0.15}

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{'loss': 1.0335, 'learning_rate': 0.0003888902944906947, 'epoch': 0.15}

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{'loss': 1.5808, 'learning_rate': 0.0003887876869007254, 'epoch': 0.15}

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{'loss': 1.3421, 'learning_rate': 0.00038868462131492326, 'epoch': 0.15}

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{'loss': 1.6144, 'learning_rate': 0.00038858109798332364, 'epoch': 0.15}

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{'loss': 1.5857, 'learning_rate': 0.00038847711715707254, 'epoch': 0.15}

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{'loss': 1.3044, 'learning_rate': 0.00038837267908842563, 'epoch': 0.15}

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{'loss': 1.3907, 'learning_rate': 0.00038826778403074807, 'epoch': 0.15}

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{'loss': 1.3099, 'learning_rate': 0.00038816243223851354, 'epoch': 0.15}

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{'loss': 1.4291, 'learning_rate': 0.0003880566239673036, 'epoch': 0.15}

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{'loss': 1.3069, 'learning_rate': 0.0003879503594738076, 'epoch': 0.15}

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{'loss': 1.3345, 'learning_rate': 0.00038784363901582134, 'epoch': 0.15}

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{'loss': 1.3937, 'learning_rate': 0.00038773646285224694, 'epoch': 0.15}

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{'loss': 1.2333, 'learning_rate': 0.000387628831243092, 'epoch': 0.15}

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{'loss': 1.2367, 'learning_rate': 0.00038752074444946895, 'epoch': 0.15}

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{'loss': 1.4973, 'learning_rate': 0.0003874122027335948, 'epoch': 0.16}

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{'loss': 1.2619, 'learning_rate': 0.00038730320635878973, 'epoch': 0.16}

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{'loss': 1.3699, 'learning_rate': 0.0003871937555894773, 'epoch': 0.16}

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{'loss': 1.2068, 'learning_rate': 0.0003870838506911832, 'epoch': 0.16}

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{'loss': 1.4411, 'learning_rate': 0.000386973491930535, 'epoch': 0.16}

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{'loss': 1.4592, 'learning_rate': 0.0003868626795752612, 'epoch': 0.16}

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{'loss': 1.4266, 'learning_rate': 0.0003867514138941908, 'epoch': 0.16}

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{'loss': 1.2687, 'learning_rate': 0.00038663969515725244, 'epoch': 0.16}

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{'loss': 1.5515, 'learning_rate': 0.000386527523635474, 'epoch': 0.16}

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{'loss': 1.4121, 'learning_rate': 0.0003864148996009817, 'epoch': 0.16}

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{'loss': 1.4067, 'learning_rate': 0.00038630182332699965, 'epoch': 0.16}

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{'loss': 1.3161, 'learning_rate': 0.00038618829508784905, 'epoch': 0.16}

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{'loss': 1.3532, 'learning_rate': 0.00038607431515894747, 'epoch': 0.16}

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{'loss': 1.5935, 'learning_rate': 0.00038595988381680843, 'epoch': 0.16}

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{'loss': 1.5683, 'learning_rate': 0.0003858450013390404, 'epoch': 0.16}

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{'loss': 1.2895, 'learning_rate': 0.0003857296680043465, 'epoch': 0.16}

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{'loss': 1.4423, 'learning_rate': 0.00038561388409252346, 'epoch': 0.16}

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{'loss': 1.5754, 'learning_rate': 0.00038549764988446113, 'epoch': 0.16}

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{'loss': 1.4581, 'learning_rate': 0.0003853809656621418, 'epoch': 0.16}

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{'loss': 1.4425, 'learning_rate': 0.00038526383170863946, 'epoch': 0.16}

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{'loss': 1.0757, 'learning_rate': 0.00038514624830811924, 'epoch': 0.16}

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{'loss': 1.5043, 'learning_rate': 0.0003850282157458365, 'epoch': 0.17}

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{'loss': 1.4347, 'learning_rate': 0.0003849097343081362, 'epoch': 0.17}

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{'loss': 1.4784, 'learning_rate': 0.00038479080428245247, 'epoch': 0.17}

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{'loss': 1.2782, 'learning_rate': 0.0003846714259573077, 'epoch': 0.17}

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{'loss': 1.3142, 'learning_rate': 0.00038455159962231156, 'epoch': 0.17}

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{'loss': 1.4472, 'learning_rate': 0.00038443132556816083, 'epoch': 0.17}

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{'loss': 1.5361, 'learning_rate': 0.0003843106040866384, 'epoch': 0.17}

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{'loss': 1.345, 'learning_rate': 0.0003841894354706127, 'epoch': 0.17}

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{'loss': 1.4903, 'learning_rate': 0.00038406782001403663, 'epoch': 0.17}

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{'loss': 1.2769, 'learning_rate': 0.00038394575801194744, 'epoch': 0.17}

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{'loss': 1.3184, 'learning_rate': 0.0003838232497604655, 'epoch': 0.17}

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{'loss': 1.338, 'learning_rate': 0.0003837002955567938, 'epoch': 0.17}

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{'loss': 1.5696, 'learning_rate': 0.00038357689569921716, 'epoch': 0.17}

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{'loss': 1.0948, 'learning_rate': 0.0003834530504871018, 'epoch': 0.17}

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{'loss': 1.2939, 'learning_rate': 0.000383328760220894, 'epoch': 0.17}

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{'loss': 1.5023, 'learning_rate': 0.00038320402520211996, 'epoch': 0.17}

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{'loss': 1.272, 'learning_rate': 0.0003830788457333848, 'epoch': 0.17}

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{'loss': 1.2972, 'learning_rate': 0.0003829532221183719, 'epoch': 0.17}

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{'loss': 1.3987, 'learning_rate': 0.00038282715466184206, 'epoch': 0.17}

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{'loss': 1.3171, 'learning_rate': 0.0003827006436696328, 'epoch': 0.17}

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{'loss': 1.2435, 'learning_rate': 0.00038257368944865776, 'epoch': 0.17}

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{'loss': 1.4025, 'learning_rate': 0.00038244629230690583, 'epoch': 0.18}

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{'loss': 1.378, 'learning_rate': 0.00038231845255344034, 'epoch': 0.18}

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{'loss': 1.5586, 'learning_rate': 0.0003821901704983984, 'epoch': 0.18}

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{'loss': 1.2798, 'learning_rate': 0.00038206144645299025, 'epoch': 0.18}

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{'loss': 1.4547, 'learning_rate': 0.0003819322807294984, 'epoch': 0.18}

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{'loss': 1.3677, 'learning_rate': 0.00038180267364127654, 'epoch': 0.18}

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{'loss': 1.4257, 'learning_rate': 0.0003816726255027494, 'epoch': 0.18}

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{'loss': 1.3593, 'learning_rate': 0.00038154213662941185, 'epoch': 0.18}

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{'loss': 1.3271, 'learning_rate': 0.0003814112073378276, 'epoch': 0.18}

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{'loss': 1.5296, 'learning_rate': 0.0003812798379456289, 'epoch': 0.18}

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{'loss': 1.5515, 'learning_rate': 0.0003811480287715158, 'epoch': 0.18}

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{'loss': 1.4145, 'learning_rate': 0.0003810157801352553, 'epoch': 0.18}

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{'loss': 1.2181, 'learning_rate': 0.00038088309235768025, 'epoch': 0.18}

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{'loss': 1.4692, 'learning_rate': 0.0003807499657606891, 'epoch': 0.18}

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{'loss': 1.3907, 'learning_rate': 0.00038061640066724473, 'epoch': 0.18}

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{'loss': 1.2358, 'learning_rate': 0.0003804823974013741, 'epoch': 0.18}

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{'loss': 1.5738, 'learning_rate': 0.0003803479562881666, 'epoch': 0.18}

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{'loss': 1.3619, 'learning_rate': 0.00038021307765377443, 'epoch': 0.18}

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{'loss': 1.4637, 'learning_rate': 0.0003800777618254108, 'epoch': 0.18}

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{'loss': 1.3023, 'learning_rate': 0.0003799420091313498, 'epoch': 0.18}

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{'loss': 1.3733, 'learning_rate': 0.00037980581990092513, 'epoch': 0.18}

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{'loss': 1.5788, 'learning_rate': 0.0003796691944645296, 'epoch': 0.19}

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{'loss': 1.4521, 'learning_rate': 0.0003795321331536145, 'epoch': 0.19}

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{'loss': 1.3638, 'learning_rate': 0.0003793946363006881, 'epoch': 0.19}

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{'loss': 1.3267, 'learning_rate': 0.0003792567042393156, 'epoch': 0.19}

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{'loss': 1.5984, 'learning_rate': 0.0003791183373041179, 'epoch': 0.19}

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{'loss': 1.678, 'learning_rate': 0.00037897953583077097, 'epoch': 0.19}

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{'loss': 1.284, 'learning_rate': 0.0003788403001560048, 'epoch': 0.19}

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{'loss': 1.3238, 'learning_rate': 0.00037870063061760294, 'epoch': 0.19}

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{'loss': 1.4447, 'learning_rate': 0.0003785605275544014, 'epoch': 0.19}

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{'loss': 1.4148, 'learning_rate': 0.0003784199913062879, 'epoch': 0.19}

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{'loss': 1.3677, 'learning_rate': 0.000378279022214201, 'epoch': 0.19}

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{'loss': 1.3583, 'learning_rate': 0.0003781376206201295, 'epoch': 0.19}

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{'loss': 1.3407, 'learning_rate': 0.00037799578686711135, 'epoch': 0.19}

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{'loss': 1.4658, 'learning_rate': 0.00037785352129923283, 'epoch': 0.19}

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{'loss': 1.4504, 'learning_rate': 0.00037771082426162793, 'epoch': 0.19}

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{'loss': 1.332, 'learning_rate': 0.00037713572835324145, 'epoch': 0.19}

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{'loss': 1.5948, 'learning_rate': 0.00037669989305690835, 'epoch': 0.2}

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{'loss': 1.326, 'learning_rate': 0.0003762601997274263, 'epoch': 0.2}

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{'loss': 1.3651, 'learning_rate': 0.0003761127796004343, 'epoch': 0.2}

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{'loss': 1.384, 'learning_rate': 0.00037431051615319656, 'epoch': 0.2}

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{'loss': 1.5968, 'learning_rate': 0.0003711717483711569, 'epoch': 0.21}

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{'loss': 1.7091, 'learning_rate': 0.0003710104240971707, 'epoch': 0.21}

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{'loss': 1.4488, 'learning_rate': 0.0003708486849546997, 'epoch': 0.21}

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{'loss': 1.485, 'learning_rate': 0.00036970492772602946, 'epoch': 0.22}

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{'loss': 1.423, 'learning_rate': 0.0003688755993328832, 'epoch': 0.22}

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{'loss': 1.112, 'learning_rate': 0.000249780217710131, 'epoch': 0.45}

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{'loss': 1.0929, 'learning_rate': 0.00024947844963492594, 'epoch': 0.45}

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{'loss': 1.4064, 'learning_rate': 0.0002491765615258134, 'epoch': 0.45}

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{'loss': 1.2253, 'learning_rate': 0.00024887455411516895, 'epoch': 0.45}

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{'loss': 1.1958, 'learning_rate': 0.0002485724281356576, 'epoch': 0.45}

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{'loss': 1.3716, 'learning_rate': 0.00024827018432023196, 'epoch': 0.45}

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{'loss': 1.1603, 'learning_rate': 0.00024796782340213075, 'epoch': 0.45}

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{'loss': 1.2685, 'learning_rate': 0.0002476653461148765, 'epoch': 0.45}

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{'loss': 1.4317, 'learning_rate': 0.0002473627531922741, 'epoch': 0.45}

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{'loss': 1.3646, 'learning_rate': 0.00024706004536840907, 'epoch': 0.45}

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{'loss': 1.2738, 'learning_rate': 0.00024675722337764565, 'epoch': 0.45}

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{'loss': 1.3745, 'learning_rate': 0.000246454287954625, 'epoch': 0.45}

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{'loss': 1.2735, 'learning_rate': 0.0002461512398342635, 'epoch': 0.45}

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{'loss': 1.3064, 'learning_rate': 0.0002458480797517509, 'epoch': 0.45}

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{'loss': 1.5011, 'learning_rate': 0.0002455448084425486, 'epoch': 0.45}

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{'loss': 1.3956, 'learning_rate': 0.00024524142664238775, 'epoch': 0.45}

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{'loss': 0.9758, 'learning_rate': 0.0002449379350872678, 'epoch': 0.45}

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{'loss': 1.2169, 'learning_rate': 0.00024463433451345413, 'epoch': 0.46}

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{'loss': 1.5301, 'learning_rate': 0.0002443306256574768, 'epoch': 0.46}

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{'loss': 1.2099, 'learning_rate': 0.00024402680925612855, 'epoch': 0.46}

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{'loss': 1.4701, 'learning_rate': 0.0002437228860464629, 'epoch': 0.46}

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{'loss': 1.3601, 'learning_rate': 0.00024341885676579272, 'epoch': 0.46}

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{'loss': 1.1317, 'learning_rate': 0.0002431147221516879, 'epoch': 0.46}

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{'loss': 1.1306, 'learning_rate': 0.00024281048294197413, 'epoch': 0.46}

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{'loss': 1.3336, 'learning_rate': 0.00024250613987473067, 'epoch': 0.46}

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{'loss': 1.2368, 'learning_rate': 0.00024220169368828888, 'epoch': 0.46}

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{'loss': 1.256, 'learning_rate': 0.0002418971451212302, 'epoch': 0.46}

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{'loss': 1.3063, 'learning_rate': 0.00024159249491238446, 'epoch': 0.46}

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{'loss': 1.2309, 'learning_rate': 0.000241287743800828, 'epoch': 0.46}

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{'loss': 1.2604, 'learning_rate': 0.00024098289252588208, 'epoch': 0.46}

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{'loss': 1.4222, 'learning_rate': 0.00024067794182711097, 'epoch': 0.46}

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{'loss': 1.5747, 'learning_rate': 0.00024037289244431995, 'epoch': 0.46}

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{'loss': 1.3778, 'learning_rate': 0.00024006774511755387, 'epoch': 0.46}

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{'loss': 1.463, 'learning_rate': 0.00023976250058709505, 'epoch': 0.46}

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{'loss': 1.4231, 'learning_rate': 0.00023945715959346192, 'epoch': 0.46}

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{'loss': 1.3177, 'learning_rate': 0.00023915172287740654, 'epoch': 0.46}

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{'loss': 1.2004, 'learning_rate': 0.00023884619117991345, 'epoch': 0.46}

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{'loss': 1.1497, 'learning_rate': 0.00023854056524219748, 'epoch': 0.46}

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{'loss': 1.4359, 'learning_rate': 0.0002382348458057022, 'epoch': 0.47}

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{'loss': 1.169, 'learning_rate': 0.00023792903361209797, 'epoch': 0.47}

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{'loss': 0.9957, 'learning_rate': 0.00023762312940328012, 'epoch': 0.47}

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{'loss': 1.4934, 'learning_rate': 0.0002373171339213672, 'epoch': 0.47}

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{'loss': 1.2416, 'learning_rate': 0.00023701104790869933, 'epoch': 0.47}

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{'loss': 1.0249, 'learning_rate': 0.0002367048721078361, 'epoch': 0.47}

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{'loss': 1.4124, 'learning_rate': 0.00023639860726155497, 'epoch': 0.47}

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{'loss': 1.2547, 'learning_rate': 0.0002360922541128495, 'epoch': 0.47}

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{'loss': 1.3123, 'learning_rate': 0.00023578581340492728, 'epoch': 0.47}

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{'loss': 1.0849, 'learning_rate': 0.00023547928588120863, 'epoch': 0.47}

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{'loss': 1.3818, 'learning_rate': 0.0002351726722853241, 'epoch': 0.47}

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{'loss': 1.3689, 'learning_rate': 0.00023486597336111337, 'epoch': 0.47}

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{'loss': 1.5367, 'learning_rate': 0.00023455918985262298, 'epoch': 0.47}

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{'loss': 1.3149, 'learning_rate': 0.00023425232250410467, 'epoch': 0.47}

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{'loss': 1.4009, 'learning_rate': 0.0002339453720600136, 'epoch': 0.47}

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{'loss': 1.5073, 'learning_rate': 0.0002336383392650065, 'epoch': 0.47}

 47%|████▋     | 1000/2117 [33:35<42:21,  2.28s/it]/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
  warnings.warn(

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{'loss': 1.2735, 'learning_rate': 0.00023333122486393992, 'epoch': 0.47}

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{'loss': 1.2892, 'learning_rate': 0.00023302402960186837, 'epoch': 0.47}

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{'loss': 1.376, 'learning_rate': 0.00023271675422404249, 'epoch': 0.47}

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{'loss': 1.4852, 'learning_rate': 0.0002324093994759074, 'epoch': 0.47}

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{'loss': 1.4539, 'learning_rate': 0.00023210196610310048, 'epoch': 0.47}

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{'loss': 1.1888, 'learning_rate': 0.00023179445485145027, 'epoch': 0.48}

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{'loss': 1.109, 'learning_rate': 0.00023148686646697387, 'epoch': 0.48}

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{'loss': 1.4764, 'learning_rate': 0.00023117920169587572, 'epoch': 0.48}

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{'loss': 1.1469, 'learning_rate': 0.00023087146128454556, 'epoch': 0.48}

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{'loss': 1.1258, 'learning_rate': 0.00023056364597955648, 'epoch': 0.48}

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{'loss': 1.0952, 'learning_rate': 0.00023025575652766341, 'epoch': 0.48}

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{'loss': 1.5308, 'learning_rate': 0.0002299477936758011, 'epoch': 0.48}

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{'loss': 1.4326, 'learning_rate': 0.00022963975817108238, 'epoch': 0.48}

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{'loss': 1.203, 'learning_rate': 0.00022933165076079642, 'epoch': 0.48}

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{'loss': 1.1518, 'learning_rate': 0.00022902347219240664, 'epoch': 0.48}

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{'loss': 1.365, 'learning_rate': 0.00022871522321354912, 'epoch': 0.48}

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{'loss': 1.1922, 'learning_rate': 0.00022840690457203106, 'epoch': 0.48}

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{'loss': 1.3392, 'learning_rate': 0.00022809851701582825, 'epoch': 0.48}

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{'loss': 1.1993, 'learning_rate': 0.0002277900612930839, 'epoch': 0.48}

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{'loss': 1.3798, 'learning_rate': 0.00022748153815210654, 'epoch': 0.48}

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{'loss': 1.5262, 'learning_rate': 0.00022717294834136818, 'epoch': 0.48}

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{'loss': 1.206, 'learning_rate': 0.00022686429260950278, 'epoch': 0.48}

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{'loss': 1.1834, 'learning_rate': 0.00022655557170530398, 'epoch': 0.48}

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{'loss': 1.2888, 'learning_rate': 1.6176750239534555e-05, 'epoch': 0.88}

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{'loss': 1.0351, 'learning_rate': 1.6054241988052608e-05, 'epoch': 0.88}

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{'loss': 1.3194, 'learning_rate': 1.5932179985963412e-05, 'epoch': 0.88}

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{'loss': 1.1395, 'learning_rate': 1.581056452938736e-05, 'epoch': 0.88}

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{'loss': 1.1652, 'learning_rate': 1.5689395913361603e-05, 'epoch': 0.88}

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{'loss': 1.2574, 'learning_rate': 1.5568674431839224e-05, 'epoch': 0.88}

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{'loss': 1.3462, 'learning_rate': 1.544840037768851e-05, 'epoch': 0.88}

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{'loss': 1.5134, 'learning_rate': 1.5328574042692368e-05, 'epoch': 0.88}

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{'loss': 1.0249, 'learning_rate': 1.5209195717547486e-05, 'epoch': 0.88}

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{'loss': 1.2759, 'learning_rate': 1.5090265691863848e-05, 'epoch': 0.88}

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{'loss': 1.3408, 'learning_rate': 1.497178425416359e-05, 'epoch': 0.88}

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{'loss': 1.2896, 'learning_rate': 1.485375169188079e-05, 'epoch': 0.88}

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{'loss': 1.3258, 'learning_rate': 1.4736168291360552e-05, 'epoch': 0.88}

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{'loss': 1.3167, 'learning_rate': 1.4619034337858229e-05, 'epoch': 0.88}

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{'loss': 1.3734, 'learning_rate': 1.450235011553891e-05, 'epoch': 0.88}

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{'loss': 1.1074, 'learning_rate': 1.438611590747656e-05, 'epoch': 0.88}

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{'loss': 1.6006, 'learning_rate': 1.4270331995653507e-05, 'epoch': 0.88}

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{'loss': 1.131, 'learning_rate': 1.4154998660959596e-05, 'epoch': 0.89}

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{'loss': 1.4408, 'learning_rate': 1.4040116183191587e-05, 'epoch': 0.89}

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{'loss': 1.2286, 'learning_rate': 1.3925684841052545e-05, 'epoch': 0.89}

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{'loss': 1.1798, 'learning_rate': 1.3811704912151024e-05, 'epoch': 0.89}

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{'loss': 1.0013, 'learning_rate': 1.3698176673000351e-05, 'epoch': 0.89}

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{'loss': 1.1529, 'learning_rate': 1.3585100399018324e-05, 'epoch': 0.89}

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{'loss': 1.1891, 'learning_rate': 1.3472476364526043e-05, 'epoch': 0.89}

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{'loss': 1.3186, 'learning_rate': 1.3360304842747595e-05, 'epoch': 0.89}

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{'loss': 1.0345, 'learning_rate': 1.3248586105809235e-05, 'epoch': 0.89}

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{'loss': 1.1464, 'learning_rate': 1.3137320424738764e-05, 'epoch': 0.89}

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{'loss': 0.9625, 'learning_rate': 1.3026508069465016e-05, 'epoch': 0.89}

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{'loss': 1.3932, 'learning_rate': 1.2916149308816793e-05, 'epoch': 0.89}

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{'loss': 1.0426, 'learning_rate': 1.2806244410522739e-05, 'epoch': 0.89}

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{'loss': 1.2517, 'learning_rate': 1.2696793641210303e-05, 'epoch': 0.89}

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{'loss': 1.0455, 'learning_rate': 1.2587797266405265e-05, 'epoch': 0.89}

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{'loss': 1.3104, 'learning_rate': 1.2479255550531044e-05, 'epoch': 0.89}

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{'loss': 1.1541, 'learning_rate': 1.237116875690807e-05, 'epoch': 0.89}

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{'loss': 1.0212, 'learning_rate': 1.226353714775308e-05, 'epoch': 0.89}

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{'loss': 1.1716, 'learning_rate': 1.2156360984178672e-05, 'epoch': 0.89}

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{'loss': 1.1511, 'learning_rate': 1.2049640526192396e-05, 'epoch': 0.89}

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{'loss': 1.4378, 'learning_rate': 1.1943376032696374e-05, 'epoch': 0.89}

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{'loss': 1.1137, 'learning_rate': 1.18375677614865e-05, 'epoch': 0.9}

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{'loss': 1.2647, 'learning_rate': 1.1732215969251936e-05, 'epoch': 0.9}

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{'loss': 1.2192, 'learning_rate': 1.1627320911574369e-05, 'epoch': 0.9}

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{'loss': 1.2002, 'learning_rate': 1.1522882842927507e-05, 'epoch': 0.9}

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{'loss': 1.12, 'learning_rate': 1.1418902016676392e-05, 'epoch': 0.9}

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{'loss': 1.2486, 'learning_rate': 1.1315378685076793e-05, 'epoch': 0.9}

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{'loss': 1.2119, 'learning_rate': 1.1212313099274596e-05, 'epoch': 0.9}

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{'loss': 1.2259, 'learning_rate': 1.1109705509305256e-05, 'epoch': 0.9}

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{'loss': 1.1457, 'learning_rate': 1.1007556164093125e-05, 'epoch': 0.9}

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{'loss': 1.1518, 'learning_rate': 1.0905865311450792e-05, 'epoch': 0.9}

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{'loss': 1.0608, 'learning_rate': 1.0804633198078606e-05, 'epoch': 0.9}

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{'loss': 1.1139, 'learning_rate': 1.0703860069564009e-05, 'epoch': 0.9}

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{'loss': 1.2249, 'learning_rate': 1.0603546170381018e-05, 'epoch': 0.9}

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{'loss': 1.4376, 'learning_rate': 1.0503691743889455e-05, 'epoch': 0.9}

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{'loss': 1.115, 'learning_rate': 1.0404297032334476e-05, 'epoch': 0.9}

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{'loss': 1.1447, 'learning_rate': 1.0305362276846131e-05, 'epoch': 0.9}

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{'loss': 1.3071, 'learning_rate': 1.020688771743843e-05, 'epoch': 0.9}

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{'loss': 1.4344, 'learning_rate': 1.0108873593009071e-05, 'epoch': 0.9}

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{'loss': 1.3076, 'learning_rate': 1.0011320141338721e-05, 'epoch': 0.9}

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{'loss': 1.2095, 'learning_rate': 9.914227599090442e-06, 'epoch': 0.9}

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{'loss': 1.0488, 'learning_rate': 9.817596201809132e-06, 'epoch': 0.9}

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{'loss': 1.0581, 'learning_rate': 9.72142618392098e-06, 'epoch': 0.91}

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{'loss': 1.5264, 'learning_rate': 9.625717778732913e-06, 'epoch': 0.91}

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{'loss': 0.7503, 'learning_rate': 9.530471218431957e-06, 'epoch': 0.91}

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{'loss': 1.1666, 'learning_rate': 9.435686734084614e-06, 'epoch': 0.91}

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{'loss': 1.1909, 'learning_rate': 9.341364555636568e-06, 'epoch': 0.91}

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{'loss': 1.2914, 'learning_rate': 9.247504911911865e-06, 'epoch': 0.91}

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{'loss': 1.0331, 'learning_rate': 9.154108030612451e-06, 'epoch': 0.91}

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{'loss': 1.2396, 'learning_rate': 9.06117413831764e-06, 'epoch': 0.91}

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{'loss': 1.3323, 'learning_rate': 8.968703460483508e-06, 'epoch': 0.91}

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{'loss': 1.4465, 'learning_rate': 8.87669622144245e-06, 'epoch': 0.91}

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{'loss': 1.2658, 'learning_rate': 8.78515264440245e-06, 'epoch': 0.91}

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{'loss': 1.1626, 'learning_rate': 8.694072951446775e-06, 'epoch': 0.91}

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{'loss': 1.1752, 'learning_rate': 8.603457363533274e-06, 'epoch': 0.91}

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{'loss': 1.1018, 'learning_rate': 8.513306100493834e-06, 'epoch': 0.91}

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{'loss': 1.2596, 'learning_rate': 8.423619381033997e-06, 'epoch': 0.91}

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{'loss': 1.0342, 'learning_rate': 8.33439742273221e-06, 'epoch': 0.91}

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{'loss': 1.3798, 'learning_rate': 8.245640442039504e-06, 'epoch': 0.91}

 91%|█████████▏| 1933/2117 [1:05:14<06:14,  2.03s/it]
 91%|█████████▏| 1934/2117 [1:05:16<06:10,  2.02s/it]
                                                     
{'loss': 1.1456, 'learning_rate': 8.157348654278906e-06, 'epoch': 0.91}

 91%|█████████▏| 1934/2117 [1:05:16<06:10,  2.02s/it]
 91%|█████████▏| 1935/2117 [1:05:17<06:01,  1.99s/it]
                                                     
{'loss': 1.2221, 'learning_rate': 8.06952227364477e-06, 'epoch': 0.91}

 91%|█████████▏| 1935/2117 [1:05:17<06:01,  1.99s/it]
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{'loss': 1.2754, 'learning_rate': 7.982161513202502e-06, 'epoch': 0.91}

 91%|█████████▏| 1936/2117 [1:05:19<05:53,  1.95s/it]
 91%|█████████▏| 1937/2117 [1:05:21<05:48,  1.94s/it]
                                                     
{'loss': 0.9985, 'learning_rate': 7.895266584887884e-06, 'epoch': 0.91}

 91%|█████████▏| 1937/2117 [1:05:21<05:48,  1.94s/it]
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{'loss': 1.544, 'learning_rate': 7.808837699506556e-06, 'epoch': 0.92}

 92%|█████████▏| 1938/2117 [1:05:23<05:45,  1.93s/it]
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{'loss': 1.1587, 'learning_rate': 7.722875066733593e-06, 'epoch': 0.92}

 92%|█████████▏| 1939/2117 [1:05:25<05:53,  1.98s/it]
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{'loss': 1.3164, 'learning_rate': 7.637378895112911e-06, 'epoch': 0.92}

 92%|█████████▏| 1940/2117 [1:05:27<05:52,  1.99s/it]
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{'loss': 1.1158, 'learning_rate': 7.552349392056867e-06, 'epoch': 0.92}

 92%|█████████▏| 1941/2117 [1:05:29<05:40,  1.93s/it]
 92%|█████████▏| 1942/2117 [1:05:31<05:45,  1.97s/it]
                                                     
{'loss': 0.9729, 'learning_rate': 7.467786763845586e-06, 'epoch': 0.92}

 92%|█████████▏| 1942/2117 [1:05:31<05:45,  1.97s/it]
 92%|█████████▏| 1943/2117 [1:05:33<05:45,  1.99s/it]
                                                     
{'loss': 1.238, 'learning_rate': 7.383691215626632e-06, 'epoch': 0.92}

 92%|█████████▏| 1943/2117 [1:05:33<05:45,  1.99s/it]
 92%|█████████▏| 1944/2117 [1:05:35<05:54,  2.05s/it]
                                                     
{'loss': 1.0044, 'learning_rate': 7.300062951414455e-06, 'epoch': 0.92}

 92%|█████████▏| 1944/2117 [1:05:35<05:54,  2.05s/it]
 92%|█████████▏| 1945/2117 [1:05:37<05:40,  1.98s/it]
                                                     
{'loss': 1.0541, 'learning_rate': 7.2169021740898346e-06, 'epoch': 0.92}

 92%|█████████▏| 1945/2117 [1:05:37<05:40,  1.98s/it]
 92%|█████████▏| 1946/2117 [1:05:39<05:50,  2.05s/it]
                                                     
{'loss': 1.2909, 'learning_rate': 7.134209085399457e-06, 'epoch': 0.92}

 92%|█████████▏| 1946/2117 [1:05:39<05:50,  2.05s/it]
 92%|█████████▏| 1947/2117 [1:05:41<05:44,  2.02s/it]
                                                     
{'loss': 1.2217, 'learning_rate': 7.051983885955404e-06, 'epoch': 0.92}

 92%|█████████▏| 1947/2117 [1:05:41<05:44,  2.02s/it]
 92%|█████████▏| 1948/2117 [1:05:44<05:53,  2.09s/it]
                                                     
{'loss': 1.309, 'learning_rate': 6.970226775234623e-06, 'epoch': 0.92}

 92%|█████████▏| 1948/2117 [1:05:44<05:53,  2.09s/it]
 92%|█████████▏| 1949/2117 [1:05:46<05:59,  2.14s/it]
                                                     
{'loss': 1.2958, 'learning_rate': 6.888937951578589e-06, 'epoch': 0.92}

 92%|█████████▏| 1949/2117 [1:05:46<05:59,  2.14s/it]
 92%|█████████▏| 1950/2117 [1:05:48<06:12,  2.23s/it]
                                                     
{'loss': 0.9312, 'learning_rate': 6.808117612192599e-06, 'epoch': 0.92}

 92%|█████████▏| 1950/2117 [1:05:48<06:12,  2.23s/it]
 92%|█████████▏| 1951/2117 [1:05:51<06:22,  2.30s/it]
                                                     
{'loss': 1.1902, 'learning_rate': 6.727765953145482e-06, 'epoch': 0.92}

 92%|█████████▏| 1951/2117 [1:05:51<06:22,  2.30s/it]
 92%|█████████▏| 1952/2117 [1:05:53<05:59,  2.18s/it]
                                                     
{'loss': 1.0265, 'learning_rate': 6.647883169369085e-06, 'epoch': 0.92}

 92%|█████████▏| 1952/2117 [1:05:53<05:59,  2.18s/it]
 92%|█████████▏| 1953/2117 [1:05:55<06:00,  2.20s/it]
                                                     
{'loss': 1.1769, 'learning_rate': 6.568469454657677e-06, 'epoch': 0.92}

 92%|█████████▏| 1953/2117 [1:05:55<06:00,  2.20s/it]
 92%|█████████▏| 1954/2117 [1:05:57<05:59,  2.21s/it]
                                                     
{'loss': 1.3831, 'learning_rate': 6.4895250016676625e-06, 'epoch': 0.92}

 92%|█████████▏| 1954/2117 [1:05:57<05:59,  2.21s/it]
 92%|█████████▏| 1955/2117 [1:05:59<06:01,  2.23s/it]
                                                     
{'loss': 1.3066, 'learning_rate': 6.411050001916996e-06, 'epoch': 0.92}

 92%|█████████▏| 1955/2117 [1:05:59<06:01,  2.23s/it]
 92%|█████████▏| 1956/2117 [1:06:01<05:50,  2.18s/it]
                                                     
{'loss': 1.2549, 'learning_rate': 6.333044645784703e-06, 'epoch': 0.92}

 92%|█████████▏| 1956/2117 [1:06:01<05:50,  2.18s/it]
 92%|█████████▏| 1957/2117 [1:06:03<05:39,  2.12s/it]
                                                     
{'loss': 1.1581, 'learning_rate': 6.255509122510539e-06, 'epoch': 0.92}

 92%|█████████▏| 1957/2117 [1:06:03<05:39,  2.12s/it]
 92%|█████████▏| 1958/2117 [1:06:05<05:19,  2.01s/it]
                                                     
{'loss': 1.0347, 'learning_rate': 6.178443620194352e-06, 'epoch': 0.92}

 92%|█████████▏| 1958/2117 [1:06:05<05:19,  2.01s/it]
 93%|█████████▎| 1959/2117 [1:06:07<05:03,  1.92s/it]
                                                     
{'loss': 1.249, 'learning_rate': 6.101848325795856e-06, 'epoch': 0.93}

 93%|█████████▎| 1959/2117 [1:06:07<05:03,  1.92s/it]
 93%|█████████▎| 1960/2117 [1:06:09<05:06,  1.95s/it]
                                                     
{'loss': 1.1094, 'learning_rate': 6.025723425133989e-06, 'epoch': 0.93}

 93%|█████████▎| 1960/2117 [1:06:09<05:06,  1.95s/it]
 93%|█████████▎| 1961/2117 [1:06:11<05:04,  1.95s/it]
                                                     
{'loss': 1.363, 'learning_rate': 5.950069102886402e-06, 'epoch': 0.93}

 93%|█████████▎| 1961/2117 [1:06:11<05:04,  1.95s/it]
 93%|█████████▎| 1962/2117 [1:06:13<05:05,  1.97s/it]
                                                     
{'loss': 1.0292, 'learning_rate': 5.8748855425893475e-06, 'epoch': 0.93}

 93%|█████████▎| 1962/2117 [1:06:13<05:05,  1.97s/it]
 93%|█████████▎| 1963/2117 [1:06:15<05:08,  2.00s/it]
                                                     
{'loss': 1.0867, 'learning_rate': 5.800172926636882e-06, 'epoch': 0.93}

 93%|█████████▎| 1963/2117 [1:06:15<05:08,  2.00s/it]
 93%|█████████▎| 1964/2117 [1:06:17<05:01,  1.97s/it]
                                                     
{'loss': 1.2136, 'learning_rate': 5.7259314362805735e-06, 'epoch': 0.93}

 93%|█████████▎| 1964/2117 [1:06:17<05:01,  1.97s/it]
 93%|█████████▎| 1965/2117 [1:06:19<05:03,  2.00s/it]
                                                     
{'loss': 1.3425, 'learning_rate': 5.652161251629085e-06, 'epoch': 0.93}

 93%|█████████▎| 1965/2117 [1:06:19<05:03,  2.00s/it]
 93%|█████████▎| 1966/2117 [1:06:21<05:00,  1.99s/it]
                                                     
{'loss': 1.28, 'learning_rate': 5.578862551647612e-06, 'epoch': 0.93}

 93%|█████████▎| 1966/2117 [1:06:21<05:00,  1.99s/it]
 93%|█████████▎| 1967/2117 [1:06:23<04:57,  1.98s/it]
                                                     
{'loss': 1.2319, 'learning_rate': 5.50603551415767e-06, 'epoch': 0.93}

 93%|█████████▎| 1967/2117 [1:06:23<04:57,  1.98s/it]
 93%|█████████▎| 1968/2117 [1:06:25<05:02,  2.03s/it]
                                                     
{'loss': 1.3405, 'learning_rate': 5.4336803158364205e-06, 'epoch': 0.93}

 93%|█████████▎| 1968/2117 [1:06:25<05:02,  2.03s/it]
 93%|█████████▎| 1969/2117 [1:06:27<05:09,  2.09s/it]
                                                     
{'loss': 1.2899, 'learning_rate': 5.361797132216406e-06, 'epoch': 0.93}

 93%|█████████▎| 1969/2117 [1:06:27<05:09,  2.09s/it]
 93%|█████████▎| 1970/2117 [1:06:29<04:54,  2.01s/it]
                                                     
{'loss': 1.3527, 'learning_rate': 5.290386137685066e-06, 'epoch': 0.93}

 93%|█████████▎| 1970/2117 [1:06:29<04:54,  2.01s/it]
 93%|█████████▎| 1971/2117 [1:06:31<04:54,  2.02s/it]
                                                     
{'loss': 1.4975, 'learning_rate': 5.219447505484243e-06, 'epoch': 0.93}

 93%|█████████▎| 1971/2117 [1:06:31<04:54,  2.02s/it]
 93%|█████████▎| 1972/2117 [1:06:33<05:08,  2.13s/it]
                                                     
{'loss': 1.2036, 'learning_rate': 5.148981407709963e-06, 'epoch': 0.93}

 93%|█████████▎| 1972/2117 [1:06:33<05:08,  2.13s/it]
 93%|█████████▎| 1973/2117 [1:06:35<05:00,  2.09s/it]
                                                     
{'loss': 1.0778, 'learning_rate': 5.078988015311769e-06, 'epoch': 0.93}

 93%|█████████▎| 1973/2117 [1:06:35<05:00,  2.09s/it]
 93%|█████████▎| 1974/2117 [1:06:38<04:57,  2.08s/it]
                                                     
{'loss': 1.5373, 'learning_rate': 5.009467498092502e-06, 'epoch': 0.93}

 93%|█████████▎| 1974/2117 [1:06:38<04:57,  2.08s/it]
 93%|█████████▎| 1975/2117 [1:06:39<04:46,  2.02s/it]
                                                     
{'loss': 1.3212, 'learning_rate': 4.9404200247078485e-06, 'epoch': 0.93}

 93%|█████████▎| 1975/2117 [1:06:39<04:46,  2.02s/it]
 93%|█████████▎| 1976/2117 [1:06:41<04:32,  1.93s/it]
                                                     
{'loss': 1.0493, 'learning_rate': 4.8718457626657496e-06, 'epoch': 0.93}

 93%|█████████▎| 1976/2117 [1:06:41<04:32,  1.93s/it]
 93%|█████████▎| 1977/2117 [1:06:43<04:32,  1.94s/it]
                                                     
{'loss': 1.2744, 'learning_rate': 4.803744878326311e-06, 'epoch': 0.93}

 93%|█████████▎| 1977/2117 [1:06:43<04:32,  1.94s/it]
 93%|█████████▎| 1978/2117 [1:06:45<04:34,  1.98s/it]
                                                     
{'loss': 1.421, 'learning_rate': 4.736117536901152e-06, 'epoch': 0.93}

 93%|█████████▎| 1978/2117 [1:06:45<04:34,  1.98s/it]
 93%|█████████▎| 1979/2117 [1:06:47<04:31,  1.97s/it]
                                                     
{'loss': 0.9075, 'learning_rate': 4.66896390245306e-06, 'epoch': 0.93}

 93%|█████████▎| 1979/2117 [1:06:47<04:31,  1.97s/it]
 94%|█████████▎| 1980/2117 [1:06:49<04:35,  2.01s/it]
                                                     
{'loss': 1.3903, 'learning_rate': 4.602284137895696e-06, 'epoch': 0.93}

 94%|█████████▎| 1980/2117 [1:06:49<04:35,  2.01s/it]
 94%|█████████▎| 1981/2117 [1:06:51<04:32,  2.00s/it]
                                                     
{'loss': 1.1848, 'learning_rate': 4.53607840499306e-06, 'epoch': 0.94}

 94%|█████████▎| 1981/2117 [1:06:51<04:32,  2.00s/it]
 94%|█████████▎| 1982/2117 [1:06:53<04:24,  1.96s/it]
                                                     
{'loss': 1.0173, 'learning_rate': 4.470346864359165e-06, 'epoch': 0.94}

 94%|█████████▎| 1982/2117 [1:06:53<04:24,  1.96s/it]
 94%|█████████▎| 1983/2117 [1:06:55<04:32,  2.03s/it]
                                                     
{'loss': 1.3709, 'learning_rate': 4.40508967545763e-06, 'epoch': 0.94}

 94%|█████████▎| 1983/2117 [1:06:55<04:32,  2.03s/it]
 94%|█████████▎| 1984/2117 [1:06:57<04:20,  1.96s/it]
                                                     
{'loss': 1.0662, 'learning_rate': 4.340306996601351e-06, 'epoch': 0.94}

 94%|█████████▎| 1984/2117 [1:06:57<04:20,  1.96s/it]
 94%|█████████▍| 1985/2117 [1:06:59<04:13,  1.92s/it]
                                                     
{'loss': 1.4046, 'learning_rate': 4.2759989849520785e-06, 'epoch': 0.94}

 94%|█████████▍| 1985/2117 [1:06:59<04:13,  1.92s/it]
 94%|█████████▍| 1986/2117 [1:07:01<04:18,  1.97s/it]
                                                     
{'loss': 1.1181, 'learning_rate': 4.212165796519929e-06, 'epoch': 0.94}

 94%|█████████▍| 1986/2117 [1:07:01<04:18,  1.97s/it]
 94%|█████████▍| 1987/2117 [1:07:03<04:26,  2.05s/it]
                                                     
{'loss': 1.2173, 'learning_rate': 4.148807586163228e-06, 'epoch': 0.94}

 94%|█████████▍| 1987/2117 [1:07:03<04:26,  2.05s/it]
 94%|█████████▍| 1988/2117 [1:07:05<04:18,  2.01s/it]
                                                     
{'loss': 1.4524, 'learning_rate': 4.085924507587913e-06, 'epoch': 0.94}

 94%|█████████▍| 1988/2117 [1:07:05<04:18,  2.01s/it]
 94%|█████████▍| 1989/2117 [1:07:07<04:16,  2.01s/it]
                                                     
{'loss': 1.2748, 'learning_rate': 4.0235167133473086e-06, 'epoch': 0.94}

 94%|█████████▍| 1989/2117 [1:07:07<04:16,  2.01s/it]
 94%|█████████▍| 1990/2117 [1:07:09<04:08,  1.95s/it]
                                                     
{'loss': 1.0592, 'learning_rate': 3.96158435484173e-06, 'epoch': 0.94}

 94%|█████████▍| 1990/2117 [1:07:09<04:08,  1.95s/it]
 94%|█████████▍| 1991/2117 [1:07:11<04:05,  1.95s/it]
                                                     
{'loss': 1.3902, 'learning_rate': 3.900127582318036e-06, 'epoch': 0.94}

 94%|█████████▍| 1991/2117 [1:07:11<04:05,  1.95s/it]
 94%|█████████▍| 1992/2117 [1:07:13<04:14,  2.03s/it]
                                                     
{'loss': 1.1882, 'learning_rate': 3.839146544869321e-06, 'epoch': 0.94}

 94%|█████████▍| 1992/2117 [1:07:13<04:14,  2.03s/it]
 94%|█████████▍| 1993/2117 [1:07:15<04:12,  2.04s/it]
                                                     
{'loss': 1.1128, 'learning_rate': 3.7786413904346674e-06, 'epoch': 0.94}

 94%|█████████▍| 1993/2117 [1:07:15<04:12,  2.04s/it]
 94%|█████████▍| 1994/2117 [1:07:17<04:09,  2.03s/it]
                                                     
{'loss': 1.2136, 'learning_rate': 3.7186122657985045e-06, 'epoch': 0.94}

 94%|█████████▍| 1994/2117 [1:07:17<04:09,  2.03s/it]
 94%|█████████▍| 1995/2117 [1:07:19<04:08,  2.04s/it]
                                                     
{'loss': 1.0755, 'learning_rate': 3.659059316590541e-06, 'epoch': 0.94}

 94%|█████████▍| 1995/2117 [1:07:19<04:08,  2.04s/it]
 94%|█████████▍| 1996/2117 [1:07:21<04:04,  2.02s/it]
                                                     
{'loss': 1.3191, 'learning_rate': 3.599982687285275e-06, 'epoch': 0.94}

 94%|█████████▍| 1996/2117 [1:07:21<04:04,  2.02s/it]
 94%|█████████▍| 1997/2117 [1:07:23<04:07,  2.06s/it]
                                                     
{'loss': 0.9365, 'learning_rate': 3.5413825212015973e-06, 'epoch': 0.94}

 94%|█████████▍| 1997/2117 [1:07:23<04:07,  2.06s/it]
 94%|█████████▍| 1998/2117 [1:07:26<04:09,  2.10s/it]
                                                     
{'loss': 1.3355, 'learning_rate': 3.4832589605025666e-06, 'epoch': 0.94}

 94%|█████████▍| 1998/2117 [1:07:26<04:09,  2.10s/it]
 94%|█████████▍| 1999/2117 [1:07:28<04:07,  2.09s/it]
                                                     
{'loss': 1.4349, 'learning_rate': 3.4256121461949676e-06, 'epoch': 0.94}

 94%|█████████▍| 1999/2117 [1:07:28<04:07,  2.09s/it]
 94%|█████████▍| 2000/2117 [1:07:30<04:00,  2.06s/it]
                                                     
{'loss': 1.1091, 'learning_rate': 3.3684422181290864e-06, 'epoch': 0.94}

 94%|█████████▍| 2000/2117 [1:07:30<04:00,  2.06s/it]/home/aiops/duanky/miniconda3/envs/lora/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
  warnings.warn(

 95%|█████████▍| 2001/2117 [1:07:32<04:05,  2.12s/it]
                                                     
{'loss': 1.0598, 'learning_rate': 3.3117493149981804e-06, 'epoch': 0.94}

 95%|█████████▍| 2001/2117 [1:07:32<04:05,  2.12s/it]
 95%|█████████▍| 2002/2117 [1:07:34<03:53,  2.03s/it]
                                                     
{'loss': 1.0386, 'learning_rate': 3.2555335743382986e-06, 'epoch': 0.95}

 95%|█████████▍| 2002/2117 [1:07:34<03:53,  2.03s/it]
 95%|█████████▍| 2003/2117 [1:07:36<03:53,  2.05s/it]
                                                     
{'loss': 1.3118, 'learning_rate': 3.1997951325279497e-06, 'epoch': 0.95}

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{'loss': 1.2383, 'learning_rate': 9.020298162950358e-07, 'epoch': 0.97}

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{'loss': 1.2303, 'learning_rate': 8.727188355404092e-07, 'epoch': 0.97}

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{'loss': 1.3226, 'learning_rate': 8.438909343047696e-07, 'epoch': 0.97}

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{'loss': 1.4357, 'learning_rate': 6.556275791054889e-07, 'epoch': 0.98}

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{'loss': 1.177, 'learning_rate': 6.061895541084051e-07, 'epoch': 0.98}

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{'loss': 0.831, 'learning_rate': 5.35660605058097e-07, 'epoch': 0.98}

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{'loss': 1.2124, 'learning_rate': 5.13118674936508e-07, 'epoch': 0.98}

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{'loss': 1.3044, 'learning_rate': 4.483968088651924e-07, 'epoch': 0.98}

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{'loss': 1.2726, 'learning_rate': 4.277910027328025e-07, 'epoch': 0.98}

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{'loss': 1.058, 'learning_rate': 4.076693555070232e-07, 'epoch': 0.98}

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{'loss': 1.3026, 'learning_rate': 3.8803191600267353e-07, 'epoch': 0.98}

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{'loss': 1.2977, 'learning_rate': 3.6887873185982305e-07, 'epoch': 0.98}

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{'loss': 1.2887, 'learning_rate': 3.5020984954379223e-07, 'epoch': 0.98}

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{'loss': 1.0595, 'learning_rate': 3.3202531434497475e-07, 'epoch': 0.98}

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{'loss': 1.11, 'learning_rate': 3.1432517037879307e-07, 'epoch': 0.98}

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{'loss': 0.9806, 'learning_rate': 2.9710946058547626e-07, 'epoch': 0.98}

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{'loss': 1.2319, 'learning_rate': 2.803782267300381e-07, 'epoch': 0.98}

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{'loss': 1.3135, 'learning_rate': 2.641315094022101e-07, 'epoch': 0.98}

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{'loss': 1.11, 'learning_rate': 2.483693480162419e-07, 'epoch': 0.99}

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{'loss': 1.0388, 'learning_rate': 2.3309178081087902e-07, 'epoch': 0.99}

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{'loss': 1.2167, 'learning_rate': 2.1829884484925178e-07, 'epoch': 0.99}

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{'loss': 1.3077, 'learning_rate': 2.0399057601880878e-07, 'epoch': 0.99}

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{'loss': 1.1231, 'learning_rate': 1.9016700903111694e-07, 'epoch': 0.99}

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{'loss': 1.1667, 'learning_rate': 1.7682817742197266e-07, 'epoch': 0.99}

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{'loss': 1.1733, 'learning_rate': 1.6397411355115743e-07, 'epoch': 0.99}

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{'loss': 0.9948, 'learning_rate': 1.5160484860239354e-07, 'epoch': 0.99}

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{'loss': 1.3368, 'learning_rate': 1.3972041258334402e-07, 'epoch': 0.99}

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{'loss': 0.9832, 'learning_rate': 1.2832083432543496e-07, 'epoch': 0.99}

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{'loss': 1.3408, 'learning_rate': 1.1740614148385564e-07, 'epoch': 0.99}

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{'loss': 0.9927, 'learning_rate': 1.0697636053749182e-07, 'epoch': 0.99}

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{'loss': 1.0995, 'learning_rate': 9.703151678879252e-08, 'epoch': 0.99}

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{'loss': 1.1784, 'learning_rate': 8.757163436381444e-08, 'epoch': 0.99}

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{'loss': 1.2843, 'learning_rate': 7.859673621206654e-08, 'epoch': 0.99}

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{'loss': 1.1433, 'learning_rate': 7.010684410648783e-08, 'epoch': 0.99}

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{'loss': 1.2614, 'learning_rate': 6.210197864344736e-08, 'epoch': 0.99}

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{'loss': 0.8912, 'learning_rate': 5.4582159242588806e-08, 'epoch': 0.99}

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{'loss': 1.2502, 'learning_rate': 4.754740414687486e-08, 'epoch': 0.99}

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{'loss': 1.0157, 'learning_rate': 4.099773042254284e-08, 'epoch': 0.99}

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{'loss': 1.2728, 'learning_rate': 3.493315395894925e-08, 'epoch': 0.99}

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{'loss': 1.1902, 'learning_rate': 2.935368946868078e-08, 'epoch': 0.99}

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{'loss': 1.0384, 'learning_rate': 2.4259350487421117e-08, 'epoch': 1.0}

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{'loss': 1.1199, 'learning_rate': 1.9650149373950933e-08, 'epoch': 1.0}

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{'loss': 1.3267, 'learning_rate': 1.552609731010346e-08, 'epoch': 1.0}

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{'loss': 1.0717, 'learning_rate': 1.1887204300786715e-08, 'epoch': 1.0}

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{'loss': 0.804, 'learning_rate': 8.733479173872461e-09, 'epoch': 1.0}

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{'loss': 1.2645, 'learning_rate': 6.064929580285039e-09, 'epoch': 1.0}

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{'loss': 1.2699, 'learning_rate': 3.881561993845928e-09, 'epoch': 1.0}

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{'loss': 1.223, 'learning_rate': 2.1833817113625645e-09, 'epoch': 1.0}

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{'loss': 0.9315, 'learning_rate': 9.703928526061389e-10, 'epoch': 1.0}

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{'loss': 1.3294, 'learning_rate': 2.425983602893922e-10, 'epoch': 1.0}

100%|██████████| 2117/2117 [1:11:24<00:00,  1.91s/it]
                                                     
{'train_runtime': 4292.562, 'train_samples_per_second': 15.786, 'train_steps_per_second': 0.493, 'train_loss': 1.310140783374581, 'epoch': 1.0}

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wandb: 
wandb: Run history:
wandb:                    train/epoch ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb:              train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███
wandb:            train/learning_rate ▃▅███████▇▇▇▇▇▆▆▆▅▅▅▅▄▄▄▃▃▃▃▂▂▂▂▂▁▁▁▁▁▁▁
wandb:                     train/loss ▆▆▅█▇▇▇▇▇▇▆▇▅▆▅▆▇▆▅▅▅▅▅▅▅▆▃▆▆▃▆▄▂▅▄▅▃▃▆▁
wandb:               train/total_flos ▁
wandb:               train/train_loss ▁
wandb:            train/train_runtime ▁
wandb: train/train_samples_per_second ▁
wandb:   train/train_steps_per_second ▁
wandb: 
wandb: Run summary:
wandb:                    train/epoch 1.0
wandb:              train/global_step 2117
wandb:            train/learning_rate 0.0
wandb:                     train/loss 1.3294
wandb:               train/total_flos 3.5162621858545664e+17
wandb:               train/train_loss 1.31014
wandb:            train/train_runtime 4292.562
wandb: train/train_samples_per_second 15.786
wandb:   train/train_steps_per_second 0.493
wandb: 
wandb: 🚀 View run belle_dolphine-p11 at: https://wandb.ai/nus_keyu/lora-moe/runs/koei9tvj
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-20231229_062717-koei9tvj/logs