🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
Unsloth: Your Flash Attention 2 installation seems to be broken. Using Xformers instead. No performance changes will be seen.
🦥 Unsloth Zoo will now patch everything to make training faster!
==((====))==  Unsloth 2026.4.4: Fast Qwen3_5 patching. Transformers: 5.7.0.dev0. vLLM: 0.19.1.
   \\   /|    NVIDIA H200 NVL. Num GPUs = 1. Max memory: 139.801 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.10.0+cu128. CUDA: 9.0. CUDA Toolkit: 12.8. Triton: 3.6.0
\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.35. FA2 = False]
 "-____-"     Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
[transformers] The fast path is not available because one of the required library is not installed. Falling back to torch implementation. To install follow https://github.com/fla-org/flash-linear-attention#installation and https://github.com/Dao-AILab/causal-conv1d
Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.

Loading weights:   0%|          | 0/1184 [00:00<?, ?it/s]
Loading weights:   0%|          | 1/1184 [00:00<17:53,  1.10it/s]
Loading weights:   1%|          | 14/1184 [00:01<01:04, 18.07it/s]
Loading weights:   2%|▏         | 27/1184 [00:01<00:33, 34.78it/s]
Loading weights:   3%|▎         | 38/1184 [00:01<00:24, 47.52it/s]
Loading weights:   4%|▍         | 48/1184 [00:01<00:24, 46.99it/s]
Loading weights:   5%|▌         | 63/1184 [00:01<00:17, 63.46it/s]
Loading weights:   7%|▋         | 77/1184 [00:01<00:17, 63.63it/s]
Loading weights:   8%|▊         | 93/1184 [00:01<00:13, 80.41it/s]
Loading weights:   9%|▉         | 104/1184 [00:02<00:14, 74.64it/s]
Loading weights:  10%|█         | 120/1184 [00:02<00:11, 90.12it/s]
Loading weights:  11%|█         | 132/1184 [00:02<00:11, 93.01it/s]
Loading weights:  12%|█▏        | 143/1184 [00:02<00:12, 83.39it/s]
Loading weights:  13%|█▎        | 153/1184 [00:02<00:13, 78.60it/s]
Loading weights:  14%|█▍        | 165/1184 [00:02<00:14, 70.06it/s]
Loading weights:  15%|█▍        | 173/1184 [00:02<00:14, 70.26it/s]
Loading weights:  15%|█▌        | 183/1184 [00:03<00:13, 74.36it/s]
Loading weights:  17%|█▋        | 197/1184 [00:03<00:11, 82.95it/s]
Loading weights:  17%|█▋        | 206/1184 [00:03<00:13, 70.86it/s]
Loading weights:  19%|█▉        | 224/1184 [00:03<00:11, 84.99it/s]
Loading weights:  20%|█▉        | 236/1184 [00:03<00:11, 83.71it/s]
Loading weights:  21%|██        | 249/1184 [00:03<00:10, 91.60it/s]
Loading weights:  22%|██▏       | 259/1184 [00:03<00:10, 86.74it/s]
Loading weights:  23%|██▎       | 274/1184 [00:04<00:09, 98.83it/s]
Loading weights:  24%|██▍       | 285/1184 [00:04<00:09, 96.81it/s]
Loading weights:  25%|██▍       | 295/1184 [00:04<00:10, 86.14it/s]
Loading weights:  26%|██▌       | 306/1184 [00:04<00:10, 81.91it/s]
Loading weights:  27%|██▋       | 315/1184 [00:04<00:10, 83.44it/s]
Loading weights:  28%|██▊       | 331/1184 [00:04<00:10, 84.63it/s]
Loading weights:  29%|██▉       | 346/1184 [00:04<00:08, 94.98it/s]
Loading weights:  30%|███       | 356/1184 [00:05<00:08, 92.13it/s]
Loading weights:  31%|███       | 366/1184 [00:05<00:10, 80.61it/s]
Loading weights:  32%|███▏      | 380/1184 [00:05<00:09, 83.28it/s]
Loading weights:  33%|███▎      | 394/1184 [00:05<00:08, 94.59it/s]
Loading weights:  35%|███▍      | 409/1184 [00:05<00:07, 107.12it/s]
Loading weights:  36%|███▌      | 421/1184 [00:05<00:09, 82.87it/s] 
Loading weights:  37%|███▋      | 436/1184 [00:05<00:07, 96.22it/s]
Loading weights:  38%|███▊      | 448/1184 [00:05<00:07, 100.87it/s]
Loading weights:  39%|███▉      | 460/1184 [00:06<00:09, 76.96it/s] 
Loading weights:  40%|████      | 476/1184 [00:06<00:07, 91.89it/s]
Loading weights:  41%|████▏     | 489/1184 [00:06<00:07, 91.78it/s]
Loading weights:  43%|████▎     | 504/1184 [00:06<00:07, 88.27it/s]
Loading weights:  44%|████▍     | 518/1184 [00:06<00:07, 84.87it/s]
Loading weights:  45%|████▍     | 528/1184 [00:06<00:07, 86.85it/s]
Loading weights:  46%|████▌     | 544/1184 [00:07<00:06, 99.11it/s]
Loading weights:  47%|████▋     | 555/1184 [00:07<00:06, 100.18it/s]
Loading weights:  48%|████▊     | 566/1184 [00:07<00:06, 102.24it/s]
Loading weights:  49%|████▊     | 577/1184 [00:07<00:07, 85.09it/s] 
Loading weights:  50%|████▉     | 591/1184 [00:07<00:06, 93.33it/s]
Loading weights:  51%|█████     | 601/1184 [00:07<00:06, 94.50it/s]
Loading weights:  52%|█████▏    | 611/1184 [00:07<00:06, 94.97it/s]
Loading weights:  53%|█████▎    | 624/1184 [00:07<00:05, 100.16it/s]
Loading weights:  54%|█████▎    | 635/1184 [00:08<00:06, 83.91it/s] 
Loading weights:  55%|█████▍    | 648/1184 [00:08<00:06, 87.37it/s]
Loading weights:  56%|█████▌    | 662/1184 [00:08<00:05, 90.40it/s]
Loading weights:  57%|█████▋    | 676/1184 [00:08<00:06, 81.17it/s]
Loading weights:  58%|█████▊    | 690/1184 [00:08<00:05, 91.91it/s]
Loading weights:  59%|█████▉    | 700/1184 [00:08<00:05, 91.45it/s]
Loading weights:  60%|██████    | 713/1184 [00:08<00:04, 96.66it/s]
Loading weights:  61%|██████▏   | 726/1184 [00:09<00:04, 102.03it/s]
Loading weights:  62%|██████▏   | 737/1184 [00:09<00:05, 77.03it/s] 
Loading weights:  63%|██████▎   | 749/1184 [00:09<00:06, 71.57it/s]
Loading weights:  64%|██████▍   | 757/1184 [00:09<00:05, 71.40it/s]
Loading weights:  65%|██████▌   | 770/1184 [00:09<00:05, 80.38it/s]
Loading weights:  66%|██████▌   | 779/1184 [00:09<00:06, 61.59it/s]
Loading weights:  66%|██████▋   | 787/1184 [00:10<00:06, 59.35it/s]
Loading weights:  67%|██████▋   | 794/1184 [00:10<00:07, 55.39it/s]
Loading weights:  68%|██████▊   | 809/1184 [00:10<00:05, 70.48it/s]
Loading weights:  69%|██████▉   | 822/1184 [00:10<00:05, 68.90it/s]
Loading weights:  71%|███████   | 837/1184 [00:10<00:04, 81.34it/s]
Loading weights:  71%|███████▏  | 846/1184 [00:10<00:04, 69.80it/s]
Loading weights:  81%|████████▏ | 964/1184 [00:11<00:00, 298.63it/s]
Loading weights:  96%|█████████▌| 1135/1184 [00:11<00:00, 619.27it/s]
Loading weights: 100%|██████████| 1184/1184 [00:11<00:00, 106.15it/s]
Unsloth: Making `model.base_model.model.model.language_model` require gradients
Train examples: 52002

Unsloth: Tokenizing ["text"] (num_proc=1):   0%|          | 0/52002 [00:00<?, ? examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):   2%|▏         | 1000/52002 [00:02<02:10, 389.84 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):   4%|▍         | 2000/52002 [00:02<00:56, 877.73 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):   6%|▌         | 3000/52002 [00:02<00:33, 1464.46 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):   8%|▊         | 4000/52002 [00:02<00:22, 2118.64 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  10%|▉         | 5000/52002 [00:03<00:16, 2833.14 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  12%|█▏        | 6000/52002 [00:03<00:12, 3544.81 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  13%|█▎        | 7000/52002 [00:03<00:10, 4153.41 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  15%|█▌        | 8000/52002 [00:03<00:09, 4699.84 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  17%|█▋        | 9000/52002 [00:03<00:08, 5220.82 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  19%|█▉        | 10000/52002 [00:03<00:07, 5573.57 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  21%|██        | 11000/52002 [00:04<00:06, 5912.45 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  23%|██▎       | 12000/52002 [00:04<00:06, 6066.76 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  25%|██▍       | 13000/52002 [00:04<00:06, 6263.36 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  27%|██▋       | 14000/52002 [00:04<00:05, 6361.44 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  29%|██▉       | 15000/52002 [00:04<00:06, 6161.80 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  31%|███       | 16000/52002 [00:04<00:06, 5827.72 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  33%|███▎      | 17000/52002 [00:05<00:05, 5901.16 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  35%|███▍      | 18000/52002 [00:05<00:05, 5908.98 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  37%|███▋      | 19000/52002 [00:05<00:05, 6057.80 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  38%|███▊      | 20000/52002 [00:05<00:05, 6223.57 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  40%|████      | 21000/52002 [00:05<00:05, 6147.34 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  42%|████▏     | 22000/52002 [00:05<00:05, 5945.47 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  44%|████▍     | 23000/52002 [00:06<00:05, 5739.47 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  46%|████▌     | 24000/52002 [00:06<00:04, 5679.07 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  48%|████▊     | 25000/52002 [00:06<00:04, 5590.68 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  50%|████▉     | 26000/52002 [00:06<00:04, 5453.24 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  52%|█████▏    | 27000/52002 [00:06<00:04, 5407.60 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  54%|█████▍    | 28000/52002 [00:06<00:04, 5360.22 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  56%|█████▌    | 29000/52002 [00:07<00:04, 5314.66 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  58%|█████▊    | 30000/52002 [00:07<00:04, 5333.05 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  60%|█████▉    | 31000/52002 [00:07<00:03, 5258.88 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  62%|██████▏   | 32000/52002 [00:07<00:03, 5369.41 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  63%|██████▎   | 33000/52002 [00:07<00:03, 5381.25 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  65%|██████▌   | 34000/52002 [00:08<00:03, 5403.55 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  67%|██████▋   | 35000/52002 [00:08<00:03, 5364.88 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  69%|██████▉   | 36000/52002 [00:08<00:03, 5304.55 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  71%|███████   | 37000/52002 [00:08<00:02, 5357.25 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  73%|███████▎  | 38000/52002 [00:08<00:02, 5391.11 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  75%|███████▍  | 39000/52002 [00:09<00:02, 5431.81 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  77%|███████▋  | 40000/52002 [00:09<00:02, 5718.12 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  79%|███████▉  | 41000/52002 [00:09<00:01, 6032.90 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  81%|████████  | 42000/52002 [00:09<00:01, 6138.48 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  83%|████████▎ | 43000/52002 [00:09<00:01, 6273.24 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  85%|████████▍ | 44000/52002 [00:09<00:01, 6183.44 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  87%|████████▋ | 45000/52002 [00:09<00:01, 5971.47 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  88%|████████▊ | 46000/52002 [00:10<00:01, 5859.99 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  90%|█████████ | 47000/52002 [00:10<00:00, 5706.32 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  92%|█████████▏| 48000/52002 [00:10<00:00, 5555.72 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  94%|█████████▍| 49000/52002 [00:10<00:00, 5514.46 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  96%|█████████▌| 50000/52002 [00:10<00:00, 5419.44 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1):  98%|█████████▊| 51000/52002 [00:11<00:00, 5474.42 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1): 100%|█████████▉| 52000/52002 [00:11<00:00, 5406.38 examples/s]
Unsloth: Tokenizing ["text"] (num_proc=1): 100%|██████████| 52002/52002 [00:11<00:00, 4543.72 examples/s]

Map (num_proc=64):   0%|          | 0/52002 [00:00<?, ? examples/s]
Map (num_proc=64):   2%|▏         | 813/52002 [00:01<01:42, 500.35 examples/s]
Map (num_proc=64):  33%|███▎      | 17073/52002 [00:01<00:02, 13572.98 examples/s]
Map (num_proc=64):  77%|███████▋  | 39821/52002 [00:01<00:00, 35669.41 examples/s]
Map (num_proc=64): 100%|██████████| 52002/52002 [00:02<00:00, 23924.16 examples/s]

Filter (num_proc=64):   0%|          | 0/52002 [00:00<?, ? examples/s]
Filter (num_proc=64):   2%|▏         | 813/52002 [00:01<02:04, 412.28 examples/s]
Filter (num_proc=64):  25%|██▌       | 13008/52002 [00:02<00:04, 8580.47 examples/s]
Filter (num_proc=64):  56%|█████▋    | 29263/52002 [00:02<00:01, 22045.88 examples/s]
Filter (num_proc=64): 100%|██████████| 52002/52002 [00:02<00:00, 20668.64 examples/s]
[transformers] The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'eos_token_id': 248046, 'bos_token_id': None}.
[transformers] ==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1
   \\   /|    Num examples = 52,002 | Num Epochs = 1 | Total steps = 240
O^O/ \_/ \    Batch size per device = 1 | Gradient accumulation steps = 8
\        /    Data Parallel GPUs = 1 | Total batch size (1 x 8 x 1) = 8
 "-____-"     Trainable parameters = 44,171,264 of 27,400,899,824 (0.16% trained)

  0%|          | 0/240 [00:00<?, ?it/s]
  0%|          | 1/240 [00:21<1:27:15, 21.91s/it]
                                                 

  0%|          | 1/240 [00:21<1:27:15, 21.91s/it]
  1%|          | 2/240 [00:37<1:11:03, 17.91s/it]
                                                 

  1%|          | 2/240 [00:37<1:11:03, 17.91s/it]
  1%|▏         | 3/240 [00:49<1:00:57, 15.43s/it]
                                                 

  1%|▏         | 3/240 [00:49<1:00:57, 15.43s/it]
  2%|▏         | 4/240 [01:00<53:03, 13.49s/it]  
                                               

  2%|▏         | 4/240 [01:00<53:03, 13.49s/it]
  2%|▏         | 5/240 [01:11<49:45, 12.70s/it]
                                               

  2%|▏         | 5/240 [01:11<49:45, 12.70s/it]
  2%|▎         | 6/240 [01:25<51:26, 13.19s/it]
                                               

  2%|▎         | 6/240 [01:25<51:26, 13.19s/it]
  3%|▎         | 7/240 [01:39<52:54, 13.62s/it]
                                               

  3%|▎         | 7/240 [01:39<52:54, 13.62s/it]
  3%|▎         | 8/240 [01:54<54:03, 13.98s/it]
                                               

  3%|▎         | 8/240 [01:54<54:03, 13.98s/it]
  4%|▍         | 9/240 [02:08<54:10, 14.07s/it]
                                               

  4%|▍         | 9/240 [02:08<54:10, 14.07s/it]
  4%|▍         | 10/240 [02:20<50:34, 13.19s/it]
                                                

  4%|▍         | 10/240 [02:20<50:34, 13.19s/it]
  5%|▍         | 11/240 [02:31<48:40, 12.75s/it]
                                                

  5%|▍         | 11/240 [02:31<48:40, 12.75s/it]
  5%|▌         | 12/240 [02:42<46:27, 12.22s/it]
                                                

  5%|▌         | 12/240 [02:42<46:27, 12.22s/it]
  5%|▌         | 13/240 [02:57<48:41, 12.87s/it]
                                                

  5%|▌         | 13/240 [02:57<48:41, 12.87s/it]
  6%|▌         | 14/240 [03:11<49:59, 13.27s/it]
                                                

  6%|▌         | 14/240 [03:11<49:59, 13.27s/it]
  6%|▋         | 15/240 [03:25<50:28, 13.46s/it]
                                                

  6%|▋         | 15/240 [03:25<50:28, 13.46s/it]
  7%|▋         | 16/240 [03:39<50:51, 13.62s/it]
                                                

  7%|▋         | 16/240 [03:39<50:51, 13.62s/it]
  7%|▋         | 17/240 [03:50<47:33, 12.80s/it]
                                                

  7%|▋         | 17/240 [03:50<47:33, 12.80s/it]
  8%|▊         | 18/240 [04:01<45:26, 12.28s/it]
                                                

  8%|▊         | 18/240 [04:01<45:26, 12.28s/it]
  8%|▊         | 19/240 [04:13<45:17, 12.29s/it]
                                                

  8%|▊         | 19/240 [04:13<45:17, 12.29s/it]
  8%|▊         | 20/240 [04:28<47:23, 12.93s/it]
                                                

  8%|▊         | 20/240 [04:28<47:23, 12.93s/it]
  9%|▉         | 21/240 [04:42<48:23, 13.26s/it]
                                                

  9%|▉         | 21/240 [04:42<48:23, 13.26s/it]
  9%|▉         | 22/240 [04:56<49:29, 13.62s/it]
                                                

  9%|▉         | 22/240 [04:56<49:29, 13.62s/it]
 10%|▉         | 23/240 [05:10<50:02, 13.84s/it]
                                                

 10%|▉         | 23/240 [05:10<50:02, 13.84s/it]
 10%|█         | 24/240 [05:25<50:05, 13.91s/it]
                                                

 10%|█         | 24/240 [05:25<50:05, 13.91s/it]
 10%|█         | 25/240 [05:40<51:07, 14.27s/it]
                                                

 10%|█         | 25/240 [05:40<51:07, 14.27s/it]
 11%|█         | 26/240 [05:54<50:48, 14.25s/it]
                                                

 11%|█         | 26/240 [05:54<50:48, 14.25s/it]
 11%|█▏        | 27/240 [06:08<50:48, 14.31s/it]
                                                

 11%|█▏        | 27/240 [06:08<50:48, 14.31s/it]
 12%|█▏        | 28/240 [06:22<50:23, 14.26s/it]
                                                

 12%|█▏        | 28/240 [06:22<50:23, 14.26s/it]
 12%|█▏        | 29/240 [06:37<50:03, 14.23s/it]
                                                

 12%|█▏        | 29/240 [06:37<50:03, 14.23s/it]
 12%|█▎        | 30/240 [06:51<49:52, 14.25s/it]
                                                

 12%|█▎        | 30/240 [06:51<49:52, 14.25s/it]
 13%|█▎        | 31/240 [07:06<50:10, 14.40s/it]
                                                

 13%|█▎        | 31/240 [07:06<50:10, 14.40s/it]
 13%|█▎        | 32/240 [07:20<49:31, 14.29s/it]
                                                

 13%|█▎        | 32/240 [07:20<49:31, 14.29s/it]
 14%|█▍        | 33/240 [07:35<49:54, 14.47s/it]
                                                

 14%|█▍        | 33/240 [07:35<49:54, 14.47s/it]
 14%|█▍        | 34/240 [07:49<49:41, 14.47s/it]
                                                

 14%|█▍        | 34/240 [07:49<49:41, 14.47s/it]
 15%|█▍        | 35/240 [08:04<49:42, 14.55s/it]
                                                

 15%|█▍        | 35/240 [08:04<49:42, 14.55s/it]
 15%|█▌        | 36/240 [08:19<49:38, 14.60s/it]
                                                

 15%|█▌        | 36/240 [08:19<49:38, 14.60s/it]
 15%|█▌        | 37/240 [08:34<49:53, 14.74s/it]
                                                

 15%|█▌        | 37/240 [08:34<49:53, 14.74s/it]
 16%|█▌        | 38/240 [08:48<49:12, 14.62s/it]
                                                

 16%|█▌        | 38/240 [08:48<49:12, 14.62s/it]
 16%|█▋        | 39/240 [09:03<49:27, 14.76s/it]
                                                

 16%|█▋        | 39/240 [09:03<49:27, 14.76s/it]
 17%|█▋        | 40/240 [09:18<49:07, 14.74s/it]
                                                

 17%|█▋        | 40/240 [09:18<49:07, 14.74s/it]
 17%|█▋        | 41/240 [09:32<48:40, 14.67s/it]
                                                

 17%|█▋        | 41/240 [09:32<48:40, 14.67s/it]
 18%|█▊        | 42/240 [09:46<47:58, 14.54s/it]
                                                

 18%|█▊        | 42/240 [09:46<47:58, 14.54s/it]
 18%|█▊        | 43/240 [10:01<47:33, 14.49s/it]
                                                

 18%|█▊        | 43/240 [10:01<47:33, 14.49s/it]
 18%|█▊        | 44/240 [10:15<47:21, 14.50s/it]
                                                

 18%|█▊        | 44/240 [10:15<47:21, 14.50s/it]
 19%|█▉        | 45/240 [10:30<47:17, 14.55s/it]
                                                

 19%|█▉        | 45/240 [10:30<47:17, 14.55s/it]
 19%|█▉        | 46/240 [10:45<47:15, 14.62s/it]
                                                

 19%|█▉        | 46/240 [10:45<47:15, 14.62s/it]
 20%|█▉        | 47/240 [10:59<46:45, 14.54s/it]
                                                

 20%|█▉        | 47/240 [10:59<46:45, 14.54s/it]
 20%|██        | 48/240 [11:13<46:08, 14.42s/it]
                                                

 20%|██        | 48/240 [11:13<46:08, 14.42s/it]
 20%|██        | 49/240 [11:28<46:03, 14.47s/it]
                                                

 20%|██        | 49/240 [11:28<46:03, 14.47s/it]
 21%|██        | 50/240 [11:42<45:56, 14.51s/it]
                                                

 21%|██        | 50/240 [11:42<45:56, 14.51s/it]
 21%|██▏       | 51/240 [11:57<45:42, 14.51s/it]
                                                

 21%|██▏       | 51/240 [11:57<45:42, 14.51s/it]
 22%|██▏       | 52/240 [12:12<45:30, 14.52s/it]
                                                

 22%|██▏       | 52/240 [12:12<45:30, 14.52s/it]
 22%|██▏       | 53/240 [12:26<45:02, 14.45s/it]
                                                

 22%|██▏       | 53/240 [12:26<45:02, 14.45s/it]
 22%|██▎       | 54/240 [12:41<45:02, 14.53s/it]
                                                

 22%|██▎       | 54/240 [12:41<45:02, 14.53s/it]
 23%|██▎       | 55/240 [12:54<44:14, 14.35s/it]
                                                

 23%|██▎       | 55/240 [12:54<44:14, 14.35s/it]
 23%|██▎       | 56/240 [13:08<43:40, 14.24s/it]
                                                

 23%|██▎       | 56/240 [13:08<43:40, 14.24s/it]
 24%|██▍       | 57/240 [13:23<43:56, 14.41s/it]
                                                

 24%|██▍       | 57/240 [13:23<43:56, 14.41s/it]
 24%|██▍       | 58/240 [13:35<41:34, 13.70s/it]
                                                

 24%|██▍       | 58/240 [13:35<41:34, 13.70s/it]
 25%|██▍       | 59/240 [13:48<40:04, 13.28s/it]
                                                

 25%|██▍       | 59/240 [13:48<40:04, 13.28s/it]
 25%|██▌       | 60/240 [14:01<39:42, 13.24s/it]
                                                

 25%|██▌       | 60/240 [14:01<39:42, 13.24s/it]
 25%|██▌       | 61/240 [14:12<37:41, 12.63s/it]
                                                

 25%|██▌       | 61/240 [14:12<37:41, 12.63s/it]
 26%|██▌       | 62/240 [14:23<36:27, 12.29s/it]
                                                

 26%|██▌       | 62/240 [14:23<36:27, 12.29s/it]
 26%|██▋       | 63/240 [14:35<35:39, 12.09s/it]
                                                

 26%|██▋       | 63/240 [14:35<35:39, 12.09s/it]
 27%|██▋       | 64/240 [14:50<37:51, 12.91s/it]
                                                

 27%|██▋       | 64/240 [14:50<37:51, 12.91s/it]
 27%|██▋       | 65/240 [15:05<39:32, 13.56s/it]
                                                

 27%|██▋       | 65/240 [15:05<39:32, 13.56s/it]
 28%|██▊       | 66/240 [15:19<40:04, 13.82s/it]
                                                

 28%|██▊       | 66/240 [15:19<40:04, 13.82s/it]
 28%|██▊       | 67/240 [15:34<40:09, 13.93s/it]
                                                

 28%|██▊       | 67/240 [15:34<40:09, 13.93s/it]
 28%|██▊       | 68/240 [15:45<37:34, 13.10s/it]
                                                

 28%|██▊       | 68/240 [15:45<37:34, 13.10s/it]
 29%|██▉       | 69/240 [15:56<35:53, 12.59s/it]
                                                

 29%|██▉       | 69/240 [15:56<35:53, 12.59s/it]
 29%|██▉       | 70/240 [16:07<34:23, 12.14s/it]
                                                

 29%|██▉       | 70/240 [16:07<34:23, 12.14s/it]
 30%|██▉       | 71/240 [16:20<35:06, 12.47s/it]
                                                

 30%|██▉       | 71/240 [16:20<35:06, 12.47s/it]
 30%|███       | 72/240 [16:35<36:54, 13.18s/it]
                                                

 30%|███       | 72/240 [16:35<36:54, 13.18s/it]
 30%|███       | 73/240 [16:50<37:45, 13.57s/it]
                                                

 30%|███       | 73/240 [16:50<37:45, 13.57s/it]
 31%|███       | 74/240 [17:04<38:08, 13.79s/it]
                                                

 31%|███       | 74/240 [17:04<38:08, 13.79s/it]
 31%|███▏      | 75/240 [17:17<36:56, 13.43s/it]
                                                

 31%|███▏      | 75/240 [17:17<36:56, 13.43s/it]
 32%|███▏      | 76/240 [17:28<35:19, 12.92s/it]
                                                

 32%|███▏      | 76/240 [17:28<35:19, 12.92s/it]
 32%|███▏      | 77/240 [17:40<33:52, 12.47s/it]
                                                

 32%|███▏      | 77/240 [17:40<33:52, 12.47s/it]
 32%|███▎      | 78/240 [17:51<32:40, 12.10s/it]
                                                

 32%|███▎      | 78/240 [17:51<32:40, 12.10s/it]
 33%|███▎      | 79/240 [18:05<33:52, 12.62s/it]
                                                

 33%|███▎      | 79/240 [18:05<33:52, 12.62s/it]
 33%|███▎      | 80/240 [18:20<35:17, 13.23s/it]
                                                

 33%|███▎      | 80/240 [18:20<35:17, 13.23s/it]
 34%|███▍      | 81/240 [18:34<35:50, 13.53s/it]
                                                

 34%|███▍      | 81/240 [18:34<35:50, 13.53s/it]
 34%|███▍      | 82/240 [18:48<36:25, 13.84s/it]
                                                

 34%|███▍      | 82/240 [18:48<36:25, 13.84s/it]
 35%|███▍      | 83/240 [19:00<34:36, 13.23s/it]
                                                

 35%|███▍      | 83/240 [19:00<34:36, 13.23s/it]
 35%|███▌      | 84/240 [19:11<32:42, 12.58s/it]
                                                

 35%|███▌      | 84/240 [19:11<32:42, 12.58s/it]
 35%|███▌      | 85/240 [19:22<31:15, 12.10s/it]
                                                

 35%|███▌      | 85/240 [19:22<31:15, 12.10s/it]
 36%|███▌      | 86/240 [19:35<31:22, 12.23s/it]
                                                

 36%|███▌      | 86/240 [19:35<31:22, 12.23s/it]
 36%|███▋      | 87/240 [19:49<32:47, 12.86s/it]
                                                

 36%|███▋      | 87/240 [19:49<32:47, 12.86s/it]
 37%|███▋      | 88/240 [20:03<33:46, 13.33s/it]
                                                

 37%|███▋      | 88/240 [20:03<33:46, 13.33s/it]
 37%|███▋      | 89/240 [20:17<33:59, 13.51s/it]
                                                

 37%|███▋      | 89/240 [20:17<33:59, 13.51s/it]
 38%|███▊      | 90/240 [20:31<33:39, 13.46s/it]
                                                
{'loss': '1.536', 'grad_norm': '0.5593', 'learning_rate': '0', 'epoch': '0.0001538'}
{'loss': '1.402', 'grad_norm': '0.4044', 'learning_rate': '2e-05', 'epoch': '0.0003077'}
{'loss': '1.498', 'grad_norm': '0.5689', 'learning_rate': '4e-05', 'epoch': '0.0004615'}
{'loss': '1.31', 'grad_norm': '0.8211', 'learning_rate': '6e-05', 'epoch': '0.0006154'}
{'loss': '1.365', 'grad_norm': '0.6742', 'learning_rate': '8e-05', 'epoch': '0.0007692'}
Unsloth: Will smartly offload gradients to save VRAM!
{'loss': '1.283', 'grad_norm': '0.4002', 'learning_rate': '0.0001', 'epoch': '0.000923'}
{'loss': '1.249', 'grad_norm': '0.4296', 'learning_rate': '0.00012', 'epoch': '0.001077'}
{'loss': '1.329', 'grad_norm': '0.4877', 'learning_rate': '0.00014', 'epoch': '0.001231'}
{'loss': '0.8771', 'grad_norm': '0.4847', 'learning_rate': '0.00016', 'epoch': '0.001385'}
{'loss': '1.233', 'grad_norm': '0.503', 'learning_rate': '0.00018', 'epoch': '0.001538'}
{'loss': '1.128', 'grad_norm': '0.7663', 'learning_rate': '0.0002', 'epoch': '0.001692'}
{'loss': '1.309', 'grad_norm': '0.6859', 'learning_rate': '0.0001991', 'epoch': '0.001846'}
{'loss': '1.165', 'grad_norm': '0.4325', 'learning_rate': '0.0001983', 'epoch': '0.002'}
{'loss': '1.08', 'grad_norm': '0.6543', 'learning_rate': '0.0001974', 'epoch': '0.002154'}
{'loss': '0.3122', 'grad_norm': '0.4972', 'learning_rate': '0.0001965', 'epoch': '0.002308'}
{'loss': '1.058', 'grad_norm': '0.538', 'learning_rate': '0.0001957', 'epoch': '0.002461'}
{'loss': '0.8285', 'grad_norm': '0.5791', 'learning_rate': '0.0001948', 'epoch': '0.002615'}
{'loss': '1.446', 'grad_norm': '0.6653', 'learning_rate': '0.0001939', 'epoch': '0.002769'}
{'loss': '0.7235', 'grad_norm': '0.3783', 'learning_rate': '0.000193', 'epoch': '0.002923'}
{'loss': '1.227', 'grad_norm': '0.4681', 'learning_rate': '0.0001922', 'epoch': '0.003077'}
{'loss': '0.9253', 'grad_norm': '1.124', 'learning_rate': '0.0001913', 'epoch': '0.003231'}
{'loss': '0.9554', 'grad_norm': '0.4662', 'learning_rate': '0.0001904', 'epoch': '0.003384'}
{'loss': '1.002', 'grad_norm': '0.3789', 'learning_rate': '0.0001896', 'epoch': '0.003538'}
{'loss': '0.9198', 'grad_norm': '0.4806', 'learning_rate': '0.0001887', 'epoch': '0.003692'}
{'loss': '1.153', 'grad_norm': '0.3054', 'learning_rate': '0.0001878', 'epoch': '0.003846'}
{'loss': '1.359', 'grad_norm': '0.4294', 'learning_rate': '0.000187', 'epoch': '0.004'}
{'loss': '1.341', 'grad_norm': '0.3838', 'learning_rate': '0.0001861', 'epoch': '0.004154'}
{'loss': '0.9128', 'grad_norm': '0.3493', 'learning_rate': '0.0001852', 'epoch': '0.004308'}
{'loss': '1.369', 'grad_norm': '0.4091', 'learning_rate': '0.0001843', 'epoch': '0.004461'}
{'loss': '1.214', 'grad_norm': '0.3802', 'learning_rate': '0.0001835', 'epoch': '0.004615'}
{'loss': '1.119', 'grad_norm': '0.2699', 'learning_rate': '0.0001826', 'epoch': '0.004769'}
{'loss': '0.8738', 'grad_norm': '0.367', 'learning_rate': '0.0001817', 'epoch': '0.004923'}
{'loss': '1.134', 'grad_norm': '0.4432', 'learning_rate': '0.0001809', 'epoch': '0.005077'}
{'loss': '1.144', 'grad_norm': '0.2986', 'learning_rate': '0.00018', 'epoch': '0.005231'}
{'loss': '0.8872', 'grad_norm': '0.2875', 'learning_rate': '0.0001791', 'epoch': '0.005384'}
{'loss': '0.8522', 'grad_norm': '0.3147', 'learning_rate': '0.0001783', 'epoch': '0.005538'}
{'loss': '0.993', 'grad_norm': '0.3047', 'learning_rate': '0.0001774', 'epoch': '0.005692'}
{'loss': '1.02', 'grad_norm': '0.2641', 'learning_rate': '0.0001765', 'epoch': '0.005846'}
{'loss': '1.087', 'grad_norm': '0.2835', 'learning_rate': '0.0001757', 'epoch': '0.006'}
{'loss': '1.149', 'grad_norm': '0.2537', 'learning_rate': '0.0001748', 'epoch': '0.006154'}
{'loss': '1.112', 'grad_norm': '0.3118', 'learning_rate': '0.0001739', 'epoch': '0.006307'}
{'loss': '1.054', 'grad_norm': '0.389', 'learning_rate': '0.000173', 'epoch': '0.006461'}
{'loss': '1.435', 'grad_norm': '0.41', 'learning_rate': '0.0001722', 'epoch': '0.006615'}
{'loss': '1.379', 'grad_norm': '0.3787', 'learning_rate': '0.0001713', 'epoch': '0.006769'}
{'loss': '1.469', 'grad_norm': '0.2917', 'learning_rate': '0.0001704', 'epoch': '0.006923'}
{'loss': '0.9515', 'grad_norm': '0.2522', 'learning_rate': '0.0001696', 'epoch': '0.007077'}
{'loss': '0.9397', 'grad_norm': '0.4667', 'learning_rate': '0.0001687', 'epoch': '0.00723'}
{'loss': '1.19', 'grad_norm': '0.3161', 'learning_rate': '0.0001678', 'epoch': '0.007384'}
{'loss': '1.424', 'grad_norm': '0.2594', 'learning_rate': '0.000167', 'epoch': '0.007538'}
{'loss': '1.533', 'grad_norm': '0.3949', 'learning_rate': '0.0001661', 'epoch': '0.007692'}
{'loss': '0.8744', 'grad_norm': '0.2812', 'learning_rate': '0.0001652', 'epoch': '0.007846'}
{'loss': '1.429', 'grad_norm': '0.3105', 'learning_rate': '0.0001643', 'epoch': '0.008'}
{'loss': '1.109', 'grad_norm': '0.3062', 'learning_rate': '0.0001635', 'epoch': '0.008154'}
{'loss': '1.066', 'grad_norm': '0.3217', 'learning_rate': '0.0001626', 'epoch': '0.008307'}
{'loss': '0.9463', 'grad_norm': '0.3176', 'learning_rate': '0.0001617', 'epoch': '0.008461'}
{'loss': '0.7722', 'grad_norm': '0.293', 'learning_rate': '0.0001609', 'epoch': '0.008615'}
{'loss': '0.7415', 'grad_norm': '0.2319', 'learning_rate': '0.00016', 'epoch': '0.008769'}
{'loss': '1.123', 'grad_norm': '0.342', 'learning_rate': '0.0001591', 'epoch': '0.008923'}
{'loss': '1.077', 'grad_norm': '0.4292', 'learning_rate': '0.0001583', 'epoch': '0.009077'}
{'loss': '0.9816', 'grad_norm': '0.3119', 'learning_rate': '0.0001574', 'epoch': '0.00923'}
{'loss': '0.8796', 'grad_norm': '0.3144', 'learning_rate': '0.0001565', 'epoch': '0.009384'}
{'loss': '1.014', 'grad_norm': '0.3658', 'learning_rate': '0.0001557', 'epoch': '0.009538'}
{'loss': '1.114', 'grad_norm': '0.2812', 'learning_rate': '0.0001548', 'epoch': '0.009692'}
{'loss': '0.943', 'grad_norm': '0.2739', 'learning_rate': '0.0001539', 'epoch': '0.009846'}
{'loss': '0.9989', 'grad_norm': '0.2288', 'learning_rate': '0.000153', 'epoch': '0.01'}
{'loss': '1.085', 'grad_norm': '0.2813', 'learning_rate': '0.0001522', 'epoch': '0.01015'}
{'loss': '0.9731', 'grad_norm': '0.2635', 'learning_rate': '0.0001513', 'epoch': '0.01031'}
{'loss': '1.131', 'grad_norm': '0.3692', 'learning_rate': '0.0001504', 'epoch': '0.01046'}
{'loss': '0.8816', 'grad_norm': '0.2943', 'learning_rate': '0.0001496', 'epoch': '0.01061'}
{'loss': '0.8566', 'grad_norm': '0.3452', 'learning_rate': '0.0001487', 'epoch': '0.01077'}
{'loss': '1.04', 'grad_norm': '0.3201', 'learning_rate': '0.0001478', 'epoch': '0.01092'}
{'loss': '1.196', 'grad_norm': '0.2852', 'learning_rate': '0.000147', 'epoch': '0.01108'}
{'loss': '1.006', 'grad_norm': '0.327', 'learning_rate': '0.0001461', 'epoch': '0.01123'}
{'loss': '0.8518', 'grad_norm': '0.3287', 'learning_rate': '0.0001452', 'epoch': '0.01138'}
{'loss': '0.9365', 'grad_norm': '0.3861', 'learning_rate': '0.0001443', 'epoch': '0.01154'}
{'loss': '1.085', 'grad_norm': '0.4307', 'learning_rate': '0.0001435', 'epoch': '0.01169'}
{'loss': '0.9501', 'grad_norm': '0.3675', 'learning_rate': '0.0001426', 'epoch': '0.01185'}
{'loss': '1.045', 'grad_norm': '0.4001', 'learning_rate': '0.0001417', 'epoch': '0.012'}
{'loss': '1.105', 'grad_norm': '0.279', 'learning_rate': '0.0001409', 'epoch': '0.01215'}
{'loss': '0.8678', 'grad_norm': '0.3335', 'learning_rate': '0.00014', 'epoch': '0.01231'}
{'loss': '0.9993', 'grad_norm': '0.4187', 'learning_rate': '0.0001391', 'epoch': '0.01246'}
{'loss': '0.9133', 'grad_norm': '0.2843', 'learning_rate': '0.0001383', 'epoch': '0.01261'}
{'loss': '1.133', 'grad_norm': '0.2608', 'learning_rate': '0.0001374', 'epoch': '0.01277'}
{'loss': '1.048', 'grad_norm': '0.3036', 'learning_rate': '0.0001365', 'epoch': '0.01292'}
{'loss': '1.26', 'grad_norm': '0.3575', 'learning_rate': '0.0001357', 'epoch': '0.01308'}
{'loss': '0.8126', 'grad_norm': '0.3878', 'learning_rate': '0.0001348', 'epoch': '0.01323'}
{'loss': '1.004', 'grad_norm': '0.336', 'learning_rate': '0.0001339', 'epoch': '0.01338'}
{'loss': '1.172', 'grad_norm': '0.2869', 'learning_rate': '0.000133', 'epoch': '0.01354'}
{'loss': '0.6368', 'grad_norm': '0.4762', 'learning_rate': '0.0001322', 'epoch': '0.01369'}
{'loss': '0.9211', 'grad_norm': '0.2755', 'learning_rate': '0.0001313', 'epoch': '0.01385'}
 38%|███▊      | 90/240 [20:31<33:39, 13.46s/it]
 38%|███▊      | 91/240 [20:42<31:34, 12.72s/it]
                                                

 38%|███▊      | 91/240 [20:42<31:34, 12.72s/it]
 38%|███▊      | 92/240 [20:53<30:14, 12.26s/it]
                                                

 38%|███▊      | 92/240 [20:53<30:14, 12.26s/it]
 39%|███▉      | 93/240 [21:04<29:21, 11.99s/it]
                                                

 39%|███▉      | 93/240 [21:04<29:21, 11.99s/it]
 39%|███▉      | 94/240 [21:18<30:43, 12.63s/it]
                                                

 39%|███▉      | 94/240 [21:18<30:43, 12.63s/it]
 40%|███▉      | 95/240 [21:33<31:37, 13.09s/it]
                                                

 40%|███▉      | 95/240 [21:33<31:37, 13.09s/it]
 40%|████      | 96/240 [21:46<31:45, 13.23s/it]
                                                

 40%|████      | 96/240 [21:46<31:45, 13.23s/it]
 40%|████      | 97/240 [22:00<31:52, 13.38s/it]
                                                

 40%|████      | 97/240 [22:00<31:52, 13.38s/it]
 41%|████      | 98/240 [22:10<29:41, 12.55s/it]
                                                

 41%|████      | 98/240 [22:10<29:41, 12.55s/it]
 41%|████▏     | 99/240 [22:22<28:45, 12.24s/it]
                                                

 41%|████▏     | 99/240 [22:22<28:45, 12.24s/it]
 42%|████▏     | 100/240 [22:33<27:35, 11.82s/it]
                                                 

 42%|████▏     | 100/240 [22:33<27:35, 11.82s/it]
 42%|████▏     | 101/240 [22:46<28:16, 12.20s/it]
                                                 

 42%|████▏     | 101/240 [22:46<28:16, 12.20s/it]
 42%|████▎     | 102/240 [23:00<29:19, 12.75s/it]
                                                 

 42%|████▎     | 102/240 [23:00<29:19, 12.75s/it]
 43%|████▎     | 103/240 [23:14<30:13, 13.24s/it]
                                                 

 43%|████▎     | 103/240 [23:14<30:13, 13.24s/it]
 43%|████▎     | 104/240 [23:28<30:33, 13.48s/it]
                                                 

 43%|████▎     | 104/240 [23:28<30:33, 13.48s/it]
 44%|████▍     | 105/240 [23:40<29:24, 13.07s/it]
                                                 

 44%|████▍     | 105/240 [23:40<29:24, 13.07s/it]
 44%|████▍     | 106/240 [23:51<27:46, 12.44s/it]
                                                 

 44%|████▍     | 106/240 [23:51<27:46, 12.44s/it]
 45%|████▍     | 107/240 [24:02<26:33, 11.98s/it]
                                                 

 45%|████▍     | 107/240 [24:02<26:33, 11.98s/it]
 45%|████▌     | 108/240 [24:15<26:42, 12.14s/it]
                                                 

 45%|████▌     | 108/240 [24:15<26:42, 12.14s/it]
 45%|████▌     | 109/240 [24:29<27:38, 12.66s/it]
                                                 

 45%|████▌     | 109/240 [24:29<27:38, 12.66s/it]
 46%|████▌     | 110/240 [24:43<28:15, 13.04s/it]
                                                 

 46%|████▌     | 110/240 [24:43<28:15, 13.04s/it]
 46%|████▋     | 111/240 [24:57<28:46, 13.39s/it]
                                                 

 46%|████▋     | 111/240 [24:57<28:46, 13.39s/it]
 47%|████▋     | 112/240 [25:10<28:34, 13.40s/it]
                                                 

 47%|████▋     | 112/240 [25:10<28:34, 13.40s/it]
 47%|████▋     | 113/240 [25:21<26:50, 12.68s/it]
                                                 

 47%|████▋     | 113/240 [25:21<26:50, 12.68s/it]
 48%|████▊     | 114/240 [25:33<25:48, 12.29s/it]
                                                 

 48%|████▊     | 114/240 [25:33<25:48, 12.29s/it]
 48%|████▊     | 115/240 [25:43<24:36, 11.81s/it]
                                                 

 48%|████▊     | 115/240 [25:43<24:36, 11.81s/it]
 48%|████▊     | 116/240 [25:56<25:08, 12.17s/it]
                                                 

 48%|████▊     | 116/240 [25:56<25:08, 12.17s/it]
 49%|████▉     | 117/240 [26:10<25:58, 12.67s/it]
                                                 

 49%|████▉     | 117/240 [26:10<25:58, 12.67s/it]
 49%|████▉     | 118/240 [26:24<26:17, 12.93s/it]
                                                 

 49%|████▉     | 118/240 [26:24<26:17, 12.93s/it]
 50%|████▉     | 119/240 [26:38<27:07, 13.45s/it]
                                                 

 50%|████▉     | 119/240 [26:38<27:07, 13.45s/it]
 50%|█████     | 120/240 [26:53<27:18, 13.65s/it]
                                                 

 50%|█████     | 120/240 [26:53<27:18, 13.65s/it]
 50%|█████     | 121/240 [27:07<27:21, 13.79s/it]
                                                 

 50%|█████     | 121/240 [27:07<27:21, 13.79s/it]
 51%|█████     | 122/240 [27:18<25:56, 13.19s/it]
                                                 

 51%|█████     | 122/240 [27:18<25:56, 13.19s/it]
 51%|█████▏    | 123/240 [27:30<24:53, 12.77s/it]
                                                 

 51%|█████▏    | 123/240 [27:30<24:53, 12.77s/it]
 52%|█████▏    | 124/240 [27:41<23:32, 12.18s/it]
                                                 

 52%|█████▏    | 124/240 [27:41<23:32, 12.18s/it]
 52%|█████▏    | 125/240 [27:53<23:26, 12.23s/it]
                                                 

 52%|█████▏    | 125/240 [27:53<23:26, 12.23s/it]
 52%|█████▎    | 126/240 [28:07<24:11, 12.73s/it]
                                                 

 52%|█████▎    | 126/240 [28:07<24:11, 12.73s/it]
 53%|█████▎    | 127/240 [28:21<24:29, 13.01s/it]
                                                 

 53%|█████▎    | 127/240 [28:21<24:29, 13.01s/it]
 53%|█████▎    | 128/240 [28:35<24:56, 13.37s/it]
                                                 

 53%|█████▎    | 128/240 [28:35<24:56, 13.37s/it]
 54%|█████▍    | 129/240 [28:49<24:52, 13.45s/it]
                                                 

 54%|█████▍    | 129/240 [28:49<24:52, 13.45s/it]
 54%|█████▍    | 130/240 [29:00<23:11, 12.65s/it]
                                                 

 54%|█████▍    | 130/240 [29:00<23:11, 12.65s/it]
 55%|█████▍    | 131/240 [29:11<22:09, 12.20s/it]
                                                 

 55%|█████▍    | 131/240 [29:11<22:09, 12.20s/it]
 55%|█████▌    | 132/240 [29:22<21:13, 11.79s/it]
                                                 

 55%|█████▌    | 132/240 [29:22<21:13, 11.79s/it]
 55%|█████▌    | 133/240 [29:36<22:28, 12.60s/it]
                                                 

 55%|█████▌    | 133/240 [29:36<22:28, 12.60s/it]
 56%|█████▌    | 134/240 [29:50<23:04, 13.06s/it]
                                                 

 56%|█████▌    | 134/240 [29:50<23:04, 13.06s/it]
 56%|█████▋    | 135/240 [30:05<23:34, 13.47s/it]
                                                 

 56%|█████▋    | 135/240 [30:05<23:34, 13.47s/it]
 57%|█████▋    | 136/240 [30:19<23:52, 13.77s/it]
                                                 

 57%|█████▋    | 136/240 [30:19<23:52, 13.77s/it]
 57%|█████▋    | 137/240 [30:33<23:39, 13.78s/it]
                                                 

 57%|█████▋    | 137/240 [30:33<23:39, 13.78s/it]
 57%|█████▊    | 138/240 [30:44<21:54, 12.89s/it]
                                                 

 57%|█████▊    | 138/240 [30:44<21:54, 12.89s/it]
 58%|█████▊    | 139/240 [30:55<21:03, 12.51s/it]
                                                 

 58%|█████▊    | 139/240 [30:55<21:03, 12.51s/it]
 58%|█████▊    | 140/240 [31:06<19:58, 11.99s/it]
                                                 

 58%|█████▊    | 140/240 [31:06<19:58, 11.99s/it]
 59%|█████▉    | 141/240 [31:19<20:07, 12.20s/it]
                                                 

 59%|█████▉    | 141/240 [31:19<20:07, 12.20s/it]
 59%|█████▉    | 142/240 [31:33<21:03, 12.89s/it]
                                                 

 59%|█████▉    | 142/240 [31:33<21:03, 12.89s/it]
 60%|█████▉    | 143/240 [31:48<21:45, 13.46s/it]
                                                 

 60%|█████▉    | 143/240 [31:48<21:45, 13.46s/it]
 60%|██████    | 144/240 [32:03<22:02, 13.77s/it]
                                                 

 60%|██████    | 144/240 [32:03<22:02, 13.77s/it]
 60%|██████    | 145/240 [32:17<21:58, 13.88s/it]
                                                 

 60%|██████    | 145/240 [32:17<21:58, 13.88s/it]
 61%|██████    | 146/240 [32:31<21:52, 13.97s/it]
                                                 

 61%|██████    | 146/240 [32:31<21:52, 13.97s/it]
 61%|██████▏   | 147/240 [32:45<21:49, 14.08s/it]
                                                 

 61%|██████▏   | 147/240 [32:45<21:49, 14.08s/it]
 62%|██████▏   | 148/240 [33:00<21:43, 14.17s/it]
                                                 

 62%|██████▏   | 148/240 [33:00<21:43, 14.17s/it]
 62%|██████▏   | 149/240 [33:14<21:40, 14.29s/it]
                                                 

 62%|██████▏   | 149/240 [33:14<21:40, 14.29s/it]
 62%|██████▎   | 150/240 [33:28<21:23, 14.26s/it]
                                                 

 62%|██████▎   | 150/240 [33:28<21:23, 14.26s/it]
 63%|██████▎   | 151/240 [33:43<21:16, 14.35s/it]
                                                 

 63%|██████▎   | 151/240 [33:43<21:16, 14.35s/it]
 63%|██████▎   | 152/240 [33:57<21:06, 14.39s/it]
                                                 

 63%|██████▎   | 152/240 [33:57<21:06, 14.39s/it]
 64%|██████▍   | 153/240 [34:11<20:39, 14.25s/it]
                                                 

 64%|██████▍   | 153/240 [34:11<20:39, 14.25s/it]
 64%|██████▍   | 154/240 [34:26<20:31, 14.32s/it]
                                                 

 64%|██████▍   | 154/240 [34:26<20:31, 14.32s/it]
 65%|██████▍   | 155/240 [34:40<20:24, 14.41s/it]
                                                 

 65%|██████▍   | 155/240 [34:40<20:24, 14.41s/it]
 65%|██████▌   | 156/240 [34:55<20:13, 14.45s/it]
                                                 

 65%|██████▌   | 156/240 [34:55<20:13, 14.45s/it]
 65%|██████▌   | 157/240 [35:09<19:52, 14.36s/it]
                                                 

 65%|██████▌   | 157/240 [35:09<19:52, 14.36s/it]
 66%|██████▌   | 158/240 [35:23<19:34, 14.33s/it]
                                                 

 66%|██████▌   | 158/240 [35:23<19:34, 14.33s/it]
 66%|██████▋   | 159/240 [35:38<19:16, 14.28s/it]
                                                 

 66%|██████▋   | 159/240 [35:38<19:16, 14.28s/it]
 67%|██████▋   | 160/240 [35:52<18:59, 14.25s/it]
                                                 

 67%|██████▋   | 160/240 [35:52<18:59, 14.25s/it]
 67%|██████▋   | 161/240 [36:06<18:40, 14.18s/it]
                                                 

 67%|██████▋   | 161/240 [36:06<18:40, 14.18s/it]
 68%|██████▊   | 162/240 [36:20<18:21, 14.13s/it]
                                                 

 68%|██████▊   | 162/240 [36:20<18:21, 14.13s/it]
 68%|██████▊   | 163/240 [36:34<18:08, 14.14s/it]
                                                 

 68%|██████▊   | 163/240 [36:34<18:08, 14.14s/it]
 68%|██████▊   | 164/240 [36:48<18:02, 14.24s/it]
                                                 

 68%|██████▊   | 164/240 [36:48<18:02, 14.24s/it]
 69%|██████▉   | 165/240 [37:03<17:56, 14.36s/it]
                                                 

 69%|██████▉   | 165/240 [37:03<17:56, 14.36s/it]
 69%|██████▉   | 166/240 [37:17<17:42, 14.36s/it]
                                                 

 69%|██████▉   | 166/240 [37:17<17:42, 14.36s/it]
 70%|██████▉   | 167/240 [37:31<17:20, 14.25s/it]
                                                 

 70%|██████▉   | 167/240 [37:31<17:20, 14.25s/it]
 70%|███████   | 168/240 [37:46<17:04, 14.23s/it]
                                                 

 70%|███████   | 168/240 [37:46<17:04, 14.23s/it]
 70%|███████   | 169/240 [37:59<16:43, 14.13s/it]
                                                 

 70%|███████   | 169/240 [37:59<16:43, 14.13s/it]
 71%|███████   | 170/240 [38:14<16:29, 14.13s/it]
                                                 

 71%|███████   | 170/240 [38:14<16:29, 14.13s/it]
 71%|███████▏  | 171/240 [38:27<16:10, 14.07s/it]
                                                 

 71%|███████▏  | 171/240 [38:27<16:10, 14.07s/it]
 72%|███████▏  | 172/240 [38:41<15:52, 14.00s/it]
                                                 

 72%|███████▏  | 172/240 [38:41<15:52, 14.00s/it]
 72%|███████▏  | 173/240 [38:55<15:38, 14.01s/it]
                                                 

 72%|███████▏  | 173/240 [38:55<15:38, 14.01s/it]
 72%|███████▎  | 174/240 [39:10<15:34, 14.15s/it]
                                                 

 72%|███████▎  | 174/240 [39:10<15:34, 14.15s/it]
 73%|███████▎  | 175/240 [39:24<15:19, 14.14s/it]
                                                 

 73%|███████▎  | 175/240 [39:24<15:19, 14.14s/it]
 73%|███████▎  | 176/240 [39:39<15:18, 14.35s/it]
                                                 

 73%|███████▎  | 176/240 [39:39<15:18, 14.35s/it]
 74%|███████▍  | 177/240 [39:53<15:08, 14.43s/it]
                                                 

 74%|███████▍  | 177/240 [39:53<15:08, 14.43s/it]
 74%|███████▍  | 178/240 [40:09<15:07, 14.64s/it]
                                                 

 74%|███████▍  | 178/240 [40:09<15:07, 14.64s/it]
 75%|███████▍  | 179/240 [40:23<14:53, 14.65s/it]
                                                 

 75%|███████▍  | 179/240 [40:23<14:53, 14.65s/it]
 75%|███████▌  | 180/240 [40:36<14:09, 14.17s/it]
                                                 

 75%|███████▌  | 180/240 [40:36<14:09, 14.17s/it]
 75%|███████▌  | 181/240 [40:47<13:01, 13.25s/it]
                                                 
{'loss': '0.8788', 'grad_norm': '0.3776', 'learning_rate': '0.0001304', 'epoch': '0.014'}
{'loss': '1.02', 'grad_norm': '0.3908', 'learning_rate': '0.0001296', 'epoch': '0.01415'}
{'loss': '1.176', 'grad_norm': '0.282', 'learning_rate': '0.0001287', 'epoch': '0.01431'}
{'loss': '1.424', 'grad_norm': '0.3191', 'learning_rate': '0.0001278', 'epoch': '0.01446'}
{'loss': '0.7966', 'grad_norm': '0.2358', 'learning_rate': '0.000127', 'epoch': '0.01461'}
{'loss': '1.063', 'grad_norm': '0.3694', 'learning_rate': '0.0001261', 'epoch': '0.01477'}
{'loss': '1.258', 'grad_norm': '0.2886', 'learning_rate': '0.0001252', 'epoch': '0.01492'}
{'loss': '1.188', 'grad_norm': '0.3431', 'learning_rate': '0.0001243', 'epoch': '0.01508'}
{'loss': '1.238', 'grad_norm': '0.2737', 'learning_rate': '0.0001235', 'epoch': '0.01523'}
{'loss': '0.73', 'grad_norm': '0.3953', 'learning_rate': '0.0001226', 'epoch': '0.01538'}
{'loss': '1.108', 'grad_norm': '0.2423', 'learning_rate': '0.0001217', 'epoch': '0.01554'}
{'loss': '0.9361', 'grad_norm': '0.345', 'learning_rate': '0.0001209', 'epoch': '0.01569'}
{'loss': '1.093', 'grad_norm': '0.3451', 'learning_rate': '0.00012', 'epoch': '0.01585'}
{'loss': '0.9732', 'grad_norm': '0.3501', 'learning_rate': '0.0001191', 'epoch': '0.016'}
{'loss': '0.9115', 'grad_norm': '0.4894', 'learning_rate': '0.0001183', 'epoch': '0.01615'}
{'loss': '1.285', 'grad_norm': '0.4014', 'learning_rate': '0.0001174', 'epoch': '0.01631'}
{'loss': '1.297', 'grad_norm': '0.3832', 'learning_rate': '0.0001165', 'epoch': '0.01646'}
{'loss': '1.136', 'grad_norm': '0.2736', 'learning_rate': '0.0001157', 'epoch': '0.01661'}
{'loss': '0.7131', 'grad_norm': '0.3998', 'learning_rate': '0.0001148', 'epoch': '0.01677'}
{'loss': '0.9841', 'grad_norm': '0.3558', 'learning_rate': '0.0001139', 'epoch': '0.01692'}
{'loss': '1.189', 'grad_norm': '0.3386', 'learning_rate': '0.000113', 'epoch': '0.01708'}
{'loss': '0.5777', 'grad_norm': '0.3674', 'learning_rate': '0.0001122', 'epoch': '0.01723'}
{'loss': '0.8153', 'grad_norm': '0.3258', 'learning_rate': '0.0001113', 'epoch': '0.01738'}
{'loss': '1.002', 'grad_norm': '0.2919', 'learning_rate': '0.0001104', 'epoch': '0.01754'}
{'loss': '0.5577', 'grad_norm': '0.3348', 'learning_rate': '0.0001096', 'epoch': '0.01769'}
{'loss': '0.8727', 'grad_norm': '0.2864', 'learning_rate': '0.0001087', 'epoch': '0.01785'}
{'loss': '0.924', 'grad_norm': '0.3181', 'learning_rate': '0.0001078', 'epoch': '0.018'}
{'loss': '1.31', 'grad_norm': '0.413', 'learning_rate': '0.000107', 'epoch': '0.01815'}
{'loss': '1.013', 'grad_norm': '0.2913', 'learning_rate': '0.0001061', 'epoch': '0.01831'}
{'loss': '1.239', 'grad_norm': '0.3089', 'learning_rate': '0.0001052', 'epoch': '0.01846'}
{'loss': '1.063', 'grad_norm': '0.3257', 'learning_rate': '0.0001043', 'epoch': '0.01861'}
{'loss': '1.315', 'grad_norm': '0.3588', 'learning_rate': '0.0001035', 'epoch': '0.01877'}
{'loss': '0.9129', 'grad_norm': '0.2146', 'learning_rate': '0.0001026', 'epoch': '0.01892'}
{'loss': '0.8617', 'grad_norm': '0.3825', 'learning_rate': '0.0001017', 'epoch': '0.01908'}
{'loss': '1.057', 'grad_norm': '0.3588', 'learning_rate': '0.0001009', 'epoch': '0.01923'}
{'loss': '0.9697', 'grad_norm': '0.324', 'learning_rate': '0.0001', 'epoch': '0.01938'}
{'loss': '1.272', 'grad_norm': '0.3821', 'learning_rate': '9.913e-05', 'epoch': '0.01954'}
{'loss': '1.01', 'grad_norm': '0.2965', 'learning_rate': '9.826e-05', 'epoch': '0.01969'}
{'loss': '0.7459', 'grad_norm': '0.3108', 'learning_rate': '9.739e-05', 'epoch': '0.01985'}
{'loss': '0.9423', 'grad_norm': '0.3031', 'learning_rate': '9.652e-05', 'epoch': '0.02'}
{'loss': '0.8303', 'grad_norm': '0.3003', 'learning_rate': '9.565e-05', 'epoch': '0.02015'}
{'loss': '1.056', 'grad_norm': '0.2872', 'learning_rate': '9.478e-05', 'epoch': '0.02031'}
{'loss': '1.253', 'grad_norm': '0.2776', 'learning_rate': '9.391e-05', 'epoch': '0.02046'}
{'loss': '1.281', 'grad_norm': '0.3136', 'learning_rate': '9.304e-05', 'epoch': '0.02061'}
{'loss': '1.144', 'grad_norm': '0.3187', 'learning_rate': '9.217e-05', 'epoch': '0.02077'}
{'loss': '1.231', 'grad_norm': '0.2466', 'learning_rate': '9.13e-05', 'epoch': '0.02092'}
{'loss': '1.089', 'grad_norm': '0.3176', 'learning_rate': '9.043e-05', 'epoch': '0.02108'}
{'loss': '1.065', 'grad_norm': '0.3689', 'learning_rate': '8.957e-05', 'epoch': '0.02123'}
{'loss': '1.183', 'grad_norm': '0.343', 'learning_rate': '8.87e-05', 'epoch': '0.02138'}
{'loss': '0.9103', 'grad_norm': '0.3659', 'learning_rate': '8.783e-05', 'epoch': '0.02154'}
{'loss': '1.052', 'grad_norm': '0.3348', 'learning_rate': '8.696e-05', 'epoch': '0.02169'}
{'loss': '1.065', 'grad_norm': '0.3312', 'learning_rate': '8.609e-05', 'epoch': '0.02185'}
{'loss': '1.427', 'grad_norm': '0.2687', 'learning_rate': '8.522e-05', 'epoch': '0.022'}
{'loss': '0.9567', 'grad_norm': '0.2504', 'learning_rate': '8.435e-05', 'epoch': '0.02215'}
{'loss': '1.09', 'grad_norm': '0.3702', 'learning_rate': '8.348e-05', 'epoch': '0.02231'}
{'loss': '1.04', 'grad_norm': '0.3157', 'learning_rate': '8.261e-05', 'epoch': '0.02246'}
{'loss': '1.01', 'grad_norm': '0.3348', 'learning_rate': '8.174e-05', 'epoch': '0.02261'}
{'loss': '1.032', 'grad_norm': '0.3914', 'learning_rate': '8.087e-05', 'epoch': '0.02277'}
{'loss': '0.8072', 'grad_norm': '0.249', 'learning_rate': '8e-05', 'epoch': '0.02292'}
{'loss': '0.9765', 'grad_norm': '0.3406', 'learning_rate': '7.913e-05', 'epoch': '0.02308'}
{'loss': '1.216', 'grad_norm': '0.2893', 'learning_rate': '7.826e-05', 'epoch': '0.02323'}
{'loss': '0.8404', 'grad_norm': '0.3531', 'learning_rate': '7.739e-05', 'epoch': '0.02338'}
{'loss': '0.4662', 'grad_norm': '0.4646', 'learning_rate': '7.652e-05', 'epoch': '0.02354'}
{'loss': '1.212', 'grad_norm': '0.2644', 'learning_rate': '7.565e-05', 'epoch': '0.02369'}
{'loss': '1.19', 'grad_norm': '0.3139', 'learning_rate': '7.478e-05', 'epoch': '0.02385'}
{'loss': '0.8821', 'grad_norm': '0.3174', 'learning_rate': '7.391e-05', 'epoch': '0.024'}
{'loss': '1.164', 'grad_norm': '0.3069', 'learning_rate': '7.304e-05', 'epoch': '0.02415'}
{'loss': '1.129', 'grad_norm': '0.3638', 'learning_rate': '7.217e-05', 'epoch': '0.02431'}
{'loss': '0.9428', 'grad_norm': '0.3637', 'learning_rate': '7.13e-05', 'epoch': '0.02446'}
{'loss': '0.9887', 'grad_norm': '0.3369', 'learning_rate': '7.043e-05', 'epoch': '0.02461'}
{'loss': '1.168', 'grad_norm': '0.3094', 'learning_rate': '6.957e-05', 'epoch': '0.02477'}
{'loss': '1.246', 'grad_norm': '0.3518', 'learning_rate': '6.87e-05', 'epoch': '0.02492'}
{'loss': '1.022', 'grad_norm': '0.3268', 'learning_rate': '6.783e-05', 'epoch': '0.02508'}
{'loss': '1.201', 'grad_norm': '0.2434', 'learning_rate': '6.696e-05', 'epoch': '0.02523'}
{'loss': '1.09', 'grad_norm': '0.253', 'learning_rate': '6.609e-05', 'epoch': '0.02538'}
{'loss': '0.8782', 'grad_norm': '0.3393', 'learning_rate': '6.522e-05', 'epoch': '0.02554'}
{'loss': '1.089', 'grad_norm': '0.3099', 'learning_rate': '6.435e-05', 'epoch': '0.02569'}
{'loss': '1.069', 'grad_norm': '0.3995', 'learning_rate': '6.348e-05', 'epoch': '0.02585'}
{'loss': '1.333', 'grad_norm': '0.3453', 'learning_rate': '6.261e-05', 'epoch': '0.026'}
{'loss': '1.188', 'grad_norm': '0.3673', 'learning_rate': '6.174e-05', 'epoch': '0.02615'}
{'loss': '0.8274', 'grad_norm': '0.2927', 'learning_rate': '6.087e-05', 'epoch': '0.02631'}
{'loss': '1.116', 'grad_norm': '0.3715', 'learning_rate': '6e-05', 'epoch': '0.02646'}
{'loss': '1.17', 'grad_norm': '0.3076', 'learning_rate': '5.913e-05', 'epoch': '0.02661'}
{'loss': '1.346', 'grad_norm': '0.2988', 'learning_rate': '5.826e-05', 'epoch': '0.02677'}
{'loss': '0.9091', 'grad_norm': '0.278', 'learning_rate': '5.739e-05', 'epoch': '0.02692'}
{'loss': '1.084', 'grad_norm': '0.3208', 'learning_rate': '5.652e-05', 'epoch': '0.02708'}
{'loss': '1.228', 'grad_norm': '0.2879', 'learning_rate': '5.565e-05', 'epoch': '0.02723'}
{'loss': '1.244', 'grad_norm': '0.2617', 'learning_rate': '5.478e-05', 'epoch': '0.02738'}
{'loss': '1.227', 'grad_norm': '0.3458', 'learning_rate': '5.391e-05', 'epoch': '0.02754'}
{'loss': '0.8763', 'grad_norm': '0.3332', 'learning_rate': '5.304e-05', 'epoch': '0.02769'}
{'loss': '0.6638', 'grad_norm': '0.3012', 'learning_rate': '5.217e-05', 'epoch': '0.02785'}
 75%|███████▌  | 181/240 [40:47<13:01, 13.25s/it]
 76%|███████▌  | 182/240 [40:58<12:11, 12.60s/it]
                                                 

 76%|███████▌  | 182/240 [40:58<12:11, 12.60s/it]
 76%|███████▋  | 183/240 [41:11<11:53, 12.52s/it]
                                                 

 76%|███████▋  | 183/240 [41:11<11:53, 12.52s/it]
 77%|███████▋  | 184/240 [41:26<12:25, 13.31s/it]
                                                 

 77%|███████▋  | 184/240 [41:26<12:25, 13.31s/it]
 77%|███████▋  | 185/240 [41:41<12:39, 13.80s/it]
                                                 

 77%|███████▋  | 185/240 [41:41<12:39, 13.80s/it]
 78%|███████▊  | 186/240 [41:55<12:33, 13.96s/it]
                                                 

 78%|███████▊  | 186/240 [41:55<12:33, 13.96s/it]
 78%|███████▊  | 187/240 [42:09<12:10, 13.78s/it]
                                                 

 78%|███████▊  | 187/240 [42:09<12:10, 13.78s/it]
 78%|███████▊  | 188/240 [42:19<11:05, 12.79s/it]
                                                 

 78%|███████▊  | 188/240 [42:19<11:05, 12.79s/it]
 79%|███████▉  | 189/240 [42:30<10:21, 12.18s/it]
                                                 

 79%|███████▉  | 189/240 [42:30<10:21, 12.18s/it]
 79%|███████▉  | 190/240 [42:42<10:02, 12.05s/it]
                                                 

 79%|███████▉  | 190/240 [42:42<10:02, 12.05s/it]
 80%|███████▉  | 191/240 [42:56<10:22, 12.71s/it]
                                                 

 80%|███████▉  | 191/240 [42:56<10:22, 12.71s/it]
 80%|████████  | 192/240 [43:10<10:28, 13.09s/it]
                                                 

 80%|████████  | 192/240 [43:10<10:28, 13.09s/it]
 80%|████████  | 193/240 [43:24<10:32, 13.47s/it]
                                                 

 80%|████████  | 193/240 [43:24<10:32, 13.47s/it]
 81%|████████  | 194/240 [43:39<10:33, 13.77s/it]
                                                 

 81%|████████  | 194/240 [43:39<10:33, 13.77s/it]
 81%|████████▏ | 195/240 [43:50<09:44, 12.99s/it]
                                                 

 81%|████████▏ | 195/240 [43:50<09:44, 12.99s/it]
 82%|████████▏ | 196/240 [44:01<09:09, 12.49s/it]
                                                 

 82%|████████▏ | 196/240 [44:01<09:09, 12.49s/it]
 82%|████████▏ | 197/240 [44:13<08:46, 12.25s/it]
                                                 

 82%|████████▏ | 197/240 [44:13<08:46, 12.25s/it]
 82%|████████▎ | 198/240 [44:27<09:00, 12.88s/it]
                                                 

 82%|████████▎ | 198/240 [44:27<09:00, 12.88s/it]
 83%|████████▎ | 199/240 [44:41<09:05, 13.30s/it]
                                                 

 83%|████████▎ | 199/240 [44:41<09:05, 13.30s/it]
 83%|████████▎ | 200/240 [44:56<09:12, 13.81s/it]
                                                 

 83%|████████▎ | 200/240 [44:56<09:12, 13.81s/it]
 84%|████████▍ | 201/240 [45:12<09:15, 14.25s/it]
                                                 

 84%|████████▍ | 201/240 [45:12<09:15, 14.25s/it]
 84%|████████▍ | 202/240 [45:25<08:49, 13.94s/it]
                                                 

 84%|████████▍ | 202/240 [45:25<08:49, 13.94s/it]
 85%|████████▍ | 203/240 [45:36<07:59, 12.97s/it]
                                                 

 85%|████████▍ | 203/240 [45:36<07:59, 12.97s/it]
 85%|████████▌ | 204/240 [45:47<07:25, 12.38s/it]
                                                 

 85%|████████▌ | 204/240 [45:47<07:25, 12.38s/it]
 85%|████████▌ | 205/240 [45:59<07:17, 12.49s/it]
                                                 

 85%|████████▌ | 205/240 [45:59<07:17, 12.49s/it]
 86%|████████▌ | 206/240 [46:14<07:26, 13.14s/it]
                                                 

 86%|████████▌ | 206/240 [46:14<07:26, 13.14s/it]
 86%|████████▋ | 207/240 [46:29<07:34, 13.78s/it]
                                                 

 86%|████████▋ | 207/240 [46:29<07:34, 13.78s/it]
 87%|████████▋ | 208/240 [46:43<07:23, 13.86s/it]
                                                 

 87%|████████▋ | 208/240 [46:43<07:23, 13.86s/it]
 87%|████████▋ | 209/240 [46:56<06:57, 13.47s/it]
                                                 

 87%|████████▋ | 209/240 [46:56<06:57, 13.47s/it]
 88%|████████▊ | 210/240 [47:07<06:22, 12.75s/it]
                                                 

 88%|████████▊ | 210/240 [47:07<06:22, 12.75s/it]
 88%|████████▊ | 211/240 [47:18<05:56, 12.29s/it]
                                                 

 88%|████████▊ | 211/240 [47:18<05:56, 12.29s/it]
 88%|████████▊ | 212/240 [47:32<05:56, 12.72s/it]
                                                 

 88%|████████▊ | 212/240 [47:32<05:56, 12.72s/it]
 89%|████████▉ | 213/240 [47:46<05:57, 13.23s/it]
                                                 

 89%|████████▉ | 213/240 [47:46<05:57, 13.23s/it]
 89%|████████▉ | 214/240 [48:01<05:56, 13.72s/it]
                                                 

 89%|████████▉ | 214/240 [48:01<05:56, 13.72s/it]
 90%|████████▉ | 215/240 [48:16<05:49, 13.98s/it]
                                                 

 90%|████████▉ | 215/240 [48:16<05:49, 13.98s/it]
 90%|█████████ | 216/240 [48:27<05:18, 13.26s/it]
                                                 

 90%|█████████ | 216/240 [48:27<05:18, 13.26s/it]
 90%|█████████ | 217/240 [48:39<04:52, 12.72s/it]
                                                 

 90%|█████████ | 217/240 [48:39<04:52, 12.72s/it]
 91%|█████████ | 218/240 [48:50<04:29, 12.26s/it]
                                                 

 91%|█████████ | 218/240 [48:50<04:29, 12.26s/it]
 91%|█████████▏| 219/240 [49:05<04:32, 12.97s/it]
                                                 

 91%|█████████▏| 219/240 [49:05<04:32, 12.97s/it]
 92%|█████████▏| 220/240 [49:20<04:33, 13.68s/it]
                                                 

 92%|█████████▏| 220/240 [49:20<04:33, 13.68s/it]
 92%|█████████▏| 221/240 [49:35<04:27, 14.07s/it]
                                                 

 92%|█████████▏| 221/240 [49:35<04:27, 14.07s/it]
 92%|█████████▎| 222/240 [49:49<04:11, 13.99s/it]
                                                 

 92%|█████████▎| 222/240 [49:49<04:11, 13.99s/it]
 93%|█████████▎| 223/240 [50:00<03:43, 13.12s/it]
                                                 

 93%|█████████▎| 223/240 [50:00<03:43, 13.12s/it]
 93%|█████████▎| 224/240 [50:11<03:19, 12.49s/it]
                                                 

 93%|█████████▎| 224/240 [50:11<03:19, 12.49s/it]
 94%|█████████▍| 225/240 [50:22<03:00, 12.00s/it]
                                                 

 94%|█████████▍| 225/240 [50:22<03:00, 12.00s/it]
 94%|█████████▍| 226/240 [50:36<02:58, 12.75s/it]
                                                 

 94%|█████████▍| 226/240 [50:36<02:58, 12.75s/it]
 95%|█████████▍| 227/240 [50:51<02:53, 13.36s/it]
                                                 

 95%|█████████▍| 227/240 [50:51<02:53, 13.36s/it]
 95%|█████████▌| 228/240 [51:06<02:44, 13.72s/it]
                                                 

 95%|█████████▌| 228/240 [51:06<02:44, 13.72s/it]
 95%|█████████▌| 229/240 [51:20<02:32, 13.89s/it]
                                                 

 95%|█████████▌| 229/240 [51:20<02:32, 13.89s/it]
 96%|█████████▌| 230/240 [51:32<02:12, 13.27s/it]
                                                 

 96%|█████████▌| 230/240 [51:32<02:12, 13.27s/it]
 96%|█████████▋| 231/240 [51:43<01:54, 12.71s/it]
                                                 

 96%|█████████▋| 231/240 [51:43<01:54, 12.71s/it]
 97%|█████████▋| 232/240 [51:54<01:37, 12.24s/it]
                                                 

 97%|█████████▋| 232/240 [51:54<01:37, 12.24s/it]
 97%|█████████▋| 233/240 [52:09<01:29, 12.85s/it]
                                                 

 97%|█████████▋| 233/240 [52:09<01:29, 12.85s/it]
 98%|█████████▊| 234/240 [52:23<01:19, 13.23s/it]
                                                 

 98%|█████████▊| 234/240 [52:23<01:19, 13.23s/it]
 98%|█████████▊| 235/240 [52:37<01:07, 13.47s/it]
                                                 

 98%|█████████▊| 235/240 [52:37<01:07, 13.47s/it]
 98%|█████████▊| 236/240 [52:50<00:53, 13.50s/it]
                                                 

 98%|█████████▊| 236/240 [52:50<00:53, 13.50s/it]
 99%|█████████▉| 237/240 [53:01<00:38, 12.80s/it]
                                                 

 99%|█████████▉| 237/240 [53:01<00:38, 12.80s/it]
 99%|█████████▉| 238/240 [53:12<00:24, 12.22s/it]
                                                 

 99%|█████████▉| 238/240 [53:12<00:24, 12.22s/it]
100%|█████████▉| 239/240 [53:24<00:11, 11.98s/it]
                                                 

100%|█████████▉| 239/240 [53:24<00:11, 11.98s/it]
100%|██████████| 240/240 [53:38<00:00, 12.59s/it]
                                                 

100%|██████████| 240/240 [53:38<00:00, 12.59s/it]
                                                 

100%|██████████| 240/240 [53:40<00:00, 12.59s/it]
100%|██████████| 240/240 [53:40<00:00, 13.42s/it]

{'loss': '0.9421', 'grad_norm': '0.2916', 'learning_rate': '5.13e-05', 'epoch': '0.028'}
{'loss': '0.8994', 'grad_norm': '0.2978', 'learning_rate': '5.043e-05', 'epoch': '0.02815'}
{'loss': '1.016', 'grad_norm': '0.2883', 'learning_rate': '4.957e-05', 'epoch': '0.02831'}
{'loss': '1.074', 'grad_norm': '0.2657', 'learning_rate': '4.87e-05', 'epoch': '0.02846'}
{'loss': '1.222', 'grad_norm': '0.2794', 'learning_rate': '4.783e-05', 'epoch': '0.02861'}
{'loss': '0.8779', 'grad_norm': '0.3356', 'learning_rate': '4.696e-05', 'epoch': '0.02877'}
{'loss': '0.9439', 'grad_norm': '0.3682', 'learning_rate': '4.609e-05', 'epoch': '0.02892'}
{'loss': '1.275', 'grad_norm': '0.4842', 'learning_rate': '4.522e-05', 'epoch': '0.02908'}
{'loss': '0.9381', 'grad_norm': '0.2296', 'learning_rate': '4.435e-05', 'epoch': '0.02923'}
{'loss': '0.9668', 'grad_norm': '0.3022', 'learning_rate': '4.348e-05', 'epoch': '0.02938'}
{'loss': '1.096', 'grad_norm': '0.312', 'learning_rate': '4.261e-05', 'epoch': '0.02954'}
{'loss': '1.249', 'grad_norm': '0.3136', 'learning_rate': '4.174e-05', 'epoch': '0.02969'}
{'loss': '0.9319', 'grad_norm': '0.4274', 'learning_rate': '4.087e-05', 'epoch': '0.02985'}
{'loss': '0.8388', 'grad_norm': '0.2833', 'learning_rate': '4e-05', 'epoch': '0.03'}
{'loss': '1.004', 'grad_norm': '0.3216', 'learning_rate': '3.913e-05', 'epoch': '0.03015'}
{'loss': '1.07', 'grad_norm': '0.2397', 'learning_rate': '3.826e-05', 'epoch': '0.03031'}
{'loss': '1.072', 'grad_norm': '0.3081', 'learning_rate': '3.739e-05', 'epoch': '0.03046'}
{'loss': '0.9617', 'grad_norm': '0.3782', 'learning_rate': '3.652e-05', 'epoch': '0.03061'}
{'loss': '1.309', 'grad_norm': '0.2492', 'learning_rate': '3.565e-05', 'epoch': '0.03077'}
{'loss': '1.243', 'grad_norm': '0.2742', 'learning_rate': '3.478e-05', 'epoch': '0.03092'}
{'loss': '1.283', 'grad_norm': '0.2802', 'learning_rate': '3.391e-05', 'epoch': '0.03108'}
{'loss': '1.041', 'grad_norm': '0.3287', 'learning_rate': '3.304e-05', 'epoch': '0.03123'}
{'loss': '1.294', 'grad_norm': '0.2625', 'learning_rate': '3.217e-05', 'epoch': '0.03138'}
{'loss': '1.062', 'grad_norm': '0.412', 'learning_rate': '3.13e-05', 'epoch': '0.03154'}
{'loss': '0.9591', 'grad_norm': '0.306', 'learning_rate': '3.043e-05', 'epoch': '0.03169'}
{'loss': '1.503', 'grad_norm': '0.2929', 'learning_rate': '2.957e-05', 'epoch': '0.03184'}
{'loss': '0.8575', 'grad_norm': '0.3625', 'learning_rate': '2.87e-05', 'epoch': '0.032'}
{'loss': '1.172', 'grad_norm': '0.3269', 'learning_rate': '2.783e-05', 'epoch': '0.03215'}
{'loss': '0.9865', 'grad_norm': '0.2973', 'learning_rate': '2.696e-05', 'epoch': '0.03231'}
{'loss': '1.319', 'grad_norm': '0.409', 'learning_rate': '2.609e-05', 'epoch': '0.03246'}
{'loss': '1.211', 'grad_norm': '0.3212', 'learning_rate': '2.522e-05', 'epoch': '0.03261'}
{'loss': '0.8975', 'grad_norm': '0.3021', 'learning_rate': '2.435e-05', 'epoch': '0.03277'}
{'loss': '1.207', 'grad_norm': '0.3143', 'learning_rate': '2.348e-05', 'epoch': '0.03292'}
{'loss': '1.198', 'grad_norm': '0.3505', 'learning_rate': '2.261e-05', 'epoch': '0.03308'}
{'loss': '0.7388', 'grad_norm': '0.2985', 'learning_rate': '2.174e-05', 'epoch': '0.03323'}
{'loss': '1.139', 'grad_norm': '0.2573', 'learning_rate': '2.087e-05', 'epoch': '0.03338'}
{'loss': '1.339', 'grad_norm': '0.3107', 'learning_rate': '2e-05', 'epoch': '0.03354'}
{'loss': '0.7872', 'grad_norm': '0.3366', 'learning_rate': '1.913e-05', 'epoch': '0.03369'}
{'loss': '1.279', 'grad_norm': '0.3148', 'learning_rate': '1.826e-05', 'epoch': '0.03384'}
{'loss': '0.9947', 'grad_norm': '0.2993', 'learning_rate': '1.739e-05', 'epoch': '0.034'}
{'loss': '0.9444', 'grad_norm': '0.2769', 'learning_rate': '1.652e-05', 'epoch': '0.03415'}
{'loss': '0.9464', 'grad_norm': '0.3421', 'learning_rate': '1.565e-05', 'epoch': '0.03431'}
{'loss': '1.129', 'grad_norm': '0.2885', 'learning_rate': '1.478e-05', 'epoch': '0.03446'}
{'loss': '1.19', 'grad_norm': '0.314', 'learning_rate': '1.391e-05', 'epoch': '0.03461'}
{'loss': '0.9183', 'grad_norm': '0.2635', 'learning_rate': '1.304e-05', 'epoch': '0.03477'}
{'loss': '1.032', 'grad_norm': '0.3285', 'learning_rate': '1.217e-05', 'epoch': '0.03492'}
{'loss': '1.077', 'grad_norm': '0.3194', 'learning_rate': '1.13e-05', 'epoch': '0.03508'}
{'loss': '1.202', 'grad_norm': '0.2842', 'learning_rate': '1.043e-05', 'epoch': '0.03523'}
{'loss': '1.065', 'grad_norm': '0.2525', 'learning_rate': '9.565e-06', 'epoch': '0.03538'}
{'loss': '1.256', 'grad_norm': '0.2651', 'learning_rate': '8.696e-06', 'epoch': '0.03554'}
{'loss': '1.283', 'grad_norm': '0.3342', 'learning_rate': '7.826e-06', 'epoch': '0.03569'}
{'loss': '1.339', 'grad_norm': '0.3275', 'learning_rate': '6.957e-06', 'epoch': '0.03584'}
{'loss': '1.143', 'grad_norm': '0.3287', 'learning_rate': '6.087e-06', 'epoch': '0.036'}
{'loss': '0.7446', 'grad_norm': '0.4164', 'learning_rate': '5.217e-06', 'epoch': '0.03615'}
{'loss': '0.757', 'grad_norm': '0.4765', 'learning_rate': '4.348e-06', 'epoch': '0.03631'}
{'loss': '0.654', 'grad_norm': '0.2869', 'learning_rate': '3.478e-06', 'epoch': '0.03646'}
{'loss': '0.9501', 'grad_norm': '0.3737', 'learning_rate': '2.609e-06', 'epoch': '0.03661'}
{'loss': '1.266', 'grad_norm': '0.3191', 'learning_rate': '1.739e-06', 'epoch': '0.03677'}
{'loss': '1.309', 'grad_norm': '0.3359', 'learning_rate': '8.696e-07', 'epoch': '0.03692'}
{'train_runtime': '3220', 'train_samples_per_second': '0.596', 'train_steps_per_second': '0.075', 'train_loss': '1.067', 'epoch': '0.03692'}
Found HuggingFace hub cache directory: /home/mshahidul/.cache/huggingface/hub

Fetching 1 files:   0%|          | 0/1 [00:00<?, ?it/s]
Fetching 1 files: 100%|██████████| 1/1 [00:00<00:00, 13.27it/s]
Checking cache directory for required files...

Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:   0%|          | 0/15 [00:00<?, ?it/s]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:   7%|▋         | 1/15 [00:02<00:32,  2.31s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  13%|█▎        | 2/15 [00:04<00:30,  2.38s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  20%|██        | 3/15 [00:07<00:29,  2.43s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  27%|██▋       | 4/15 [00:09<00:26,  2.42s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  33%|███▎      | 5/15 [00:12<00:24,  2.44s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  40%|████      | 6/15 [00:14<00:22,  2.50s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  47%|████▋     | 7/15 [00:17<00:20,  2.56s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  53%|█████▎    | 8/15 [00:19<00:17,  2.52s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  60%|██████    | 9/15 [00:22<00:15,  2.56s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  67%|██████▋   | 10/15 [00:25<00:12,  2.57s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  73%|███████▎  | 11/15 [00:27<00:10,  2.58s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  80%|████████  | 12/15 [00:29<00:07,  2.44s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  87%|████████▋ | 13/15 [00:33<00:05,  2.76s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`:  93%|█████████▎| 14/15 [00:36<00:02,  2.86s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`: 100%|██████████| 15/15 [00:36<00:00,  2.13s/it]
Unsloth: Copying 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`: 100%|██████████| 15/15 [00:36<00:00,  2.45s/it]
Successfully copied all 15 files from cache to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`
Checking cache directory for required files...
Cache check failed: tokenizer.model not found in local cache.
Not all required files found in cache. Will proceed with downloading.

Unsloth: Preparing safetensor model files:   0%|          | 0/15 [00:00<?, ?it/s]
Unsloth: Preparing safetensor model files: 100%|██████████| 15/15 [00:00<00:00, 487709.77it/s]

Unsloth: Merging weights into 16bit:   0%|          | 0/15 [00:00<?, ?it/s]
Unsloth: Merging weights into 16bit:   7%|▋         | 1/15 [00:12<02:52, 12.30s/it]
Unsloth: Merging weights into 16bit:  13%|█▎        | 2/15 [00:27<03:03, 14.09s/it]
Unsloth: Merging weights into 16bit:  20%|██        | 3/15 [00:40<02:39, 13.30s/it]
Unsloth: Merging weights into 16bit:  27%|██▋       | 4/15 [00:50<02:13, 12.12s/it]
Unsloth: Merging weights into 16bit:  33%|███▎      | 5/15 [01:01<01:59, 11.93s/it]
Unsloth: Merging weights into 16bit:  40%|████      | 6/15 [01:11<01:39, 11.03s/it]
Unsloth: Merging weights into 16bit:  47%|████▋     | 7/15 [01:18<01:17,  9.66s/it]
Unsloth: Merging weights into 16bit:  53%|█████▎    | 8/15 [01:26<01:05,  9.40s/it]
Unsloth: Merging weights into 16bit:  60%|██████    | 9/15 [01:38<00:59, 10.00s/it]
Unsloth: Merging weights into 16bit:  67%|██████▋   | 10/15 [01:49<00:51, 10.32s/it]
Unsloth: Merging weights into 16bit:  73%|███████▎  | 11/15 [02:00<00:42, 10.74s/it]
Unsloth: Merging weights into 16bit:  80%|████████  | 12/15 [02:09<00:30, 10.14s/it]
Unsloth: Merging weights into 16bit:  87%|████████▋ | 13/15 [02:18<00:19,  9.88s/it]
Unsloth: Merging weights into 16bit:  93%|█████████▎| 14/15 [02:29<00:10, 10.11s/it]
Unsloth: Merging weights into 16bit: 100%|██████████| 15/15 [02:30<00:00,  7.47s/it]
Unsloth: Merging weights into 16bit: 100%|██████████| 15/15 [02:30<00:00, 10.06s/it]
Unsloth: Merge process complete. Saved to `/home/mshahidul/llm_safety/models/qwen3.6-alpaca`