🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
⚙️  Running in WANDB offline mode
🦥 Unsloth Zoo will now patch everything to make training faster!
==((====))==  Unsloth 2026.9.2: Fast Qwen3 patching. Transformers: 4.57.6.
   \\   /|    NVIDIA H100 NVL. Num GPUs = 1. Max memory: 93.085 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.11.0+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!
Unsloth: Dropout = 0 is supported for fast patching. You are using dropout = 0.05.
Unsloth will patch all other layers, except LoRA matrices, causing a performance hit.
Unsloth 2026.9.2 patched 36 layers with 0 QKV layers, 0 O layers and 0 MLP layers.
Unsloth: Offloading input_embeddings to disk to save VRAM
Unsloth: Offloading output_embeddings to disk to save VRAM
Unsloth: Training embed_tokens in mixed precision to save VRAM
Unsloth: Training lm_head in mixed precision to save VRAM

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supervised tokens: tool 4394688, replay 10773075 (71.0% replay)
replay capped to 25% of supervised tokens: kept 1464869 replay tokens, 1259 replay rows
rows: 18500 raw -> 10076 kept; by source: {'replay:compassion_12185_cleaned': 1259, 'recovery': 3750, 'apigen': 4567, 'efficiency': 500}
[manifest] git=6950fbbc6f29902d7fc5b26c1aef8d053966e9a3  data_sha256={'train_qwen-compassion_replay50.jsonl': '1f22b1da2039'}
wandb: WARNING git root /workspace/anchor/qwen-compassion/wandb does not exist
wandb: Tracking run with wandb version 0.30.0
wandb: W&B syncing is set to `offline` in this directory. Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Run data is saved locally in /workspace/anchor/qwen-compassion/wandb/wandb/offline-run-20261011_232644-035fmlpl
wandb: View this run in the terminal with `wandb leet`
wandb: Detected [huggingface_hub.inference] in use.
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai
==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1
   \\   /|    Num examples = 9,824 | Num Epochs = 1 | Total steps = 614
O^O/ \_/ \    Batch size per device = 2 | Gradient accumulation steps = 8
\        /    Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16
 "-____-"     Trainable parameters = 1,419,247,616 of 9,609,982,976 (14.77% trained)
Unsloth: Setting lr = 2.00e-05 instead of 2.00e-04 for embed_tokens.
Unsloth: Setting lr = 2.00e-05 instead of 2.00e-04 for lm_head.

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Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass.
{'loss': 3.3662, 'grad_norm': 22.442302703857422, 'learning_rate': 2.5806451612903226e-05, 'epoch': 0.01}
{'loss': 3.436, 'grad_norm': 14.803105354309082, 'learning_rate': 5.8064516129032266e-05, 'epoch': 0.02}
{'loss': 2.2124, 'grad_norm': 9.32910442352295, 'learning_rate': 9.032258064516129e-05, 'epoch': 0.02}
{'loss': 1.4505, 'grad_norm': 2.5130510330200195, 'learning_rate': 0.00012258064516129034, 'epoch': 0.03}
{'loss': 1.1231, 'grad_norm': 18.144712448120117, 'learning_rate': 0.00015483870967741937, 'epoch': 0.04}
{'loss': 0.872, 'grad_norm': 3.0365991592407227, 'learning_rate': 0.0001870967741935484, 'epoch': 0.05}
{'loss': 0.8906, 'grad_norm': 2.5769875049591064, 'learning_rate': 0.00019998693330688282, 'epoch': 0.06}
{'loss': 0.7195, 'grad_norm': 3.9422550201416016, 'learning_rate': 0.00019990709365929677, 'epoch': 0.07}
{'loss': 0.6868, 'grad_norm': 2.395726442337036, 'learning_rate': 0.00019975473152335726, 'epoch': 0.07}
{'loss': 0.6497, 'grad_norm': 1.726025104522705, 'learning_rate': 0.0001995299574986306, 'epoch': 0.08}


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{'loss': 0.6646, 'grad_norm': 1.482474684715271, 'learning_rate': 0.00019923293474842174, 'epoch': 0.09}
{'loss': 0.5642, 'grad_norm': 1.236023187637329, 'learning_rate': 0.00019886387888133413, 'epoch': 0.1}
{'loss': 0.4603, 'grad_norm': 1.1658198833465576, 'learning_rate': 0.00019842305779475968, 'epoch': 0.11}
{'loss': 0.6375, 'grad_norm': 1.2695726156234741, 'learning_rate': 0.0001979107914804122, 'epoch': 0.11}
{'loss': 0.4823, 'grad_norm': 1.4793083667755127, 'learning_rate': 0.00019732745179204552, 'epoch': 0.12}
{'loss': 0.4646, 'grad_norm': 1.7086373567581177, 'learning_rate': 0.00019667346217552527, 'epoch': 0.13}
{'loss': 0.6899, 'grad_norm': 1.3679929971694946, 'learning_rate': 0.00019594929736144976, 'epoch': 0.14}
{'loss': 0.5442, 'grad_norm': 1.4010539054870605, 'learning_rate': 0.00019515548302054335, 'epoch': 0.15}
{'loss': 0.5184, 'grad_norm': 1.1517243385314941, 'learning_rate': 0.0001942925953820725, 'epoch': 0.15}
{'loss': 0.6193, 'grad_norm': 1.058547019958496, 'learning_rate': 0.00019336126081556134, 'epoch': 0.16}


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{'loss': 0.6425, 'grad_norm': 0.9430150985717773, 'learning_rate': 0.00016174527739955342, 'epoch': 0.33}


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 41%|████      | 250/614 [1:31:03<1:49:07, 17.99s/it]{'eval_loss': 0.3253232538700104, 'eval_runtime': 120.2962, 'eval_samples_per_second': 2.095, 'eval_steps_per_second': 1.047, 'epoch': 0.33}
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{'loss': 0.5095, 'grad_norm': 1.0070925951004028, 'learning_rate': 0.00014590246098055996, 'epoch': 0.38}
{'loss': 0.3591, 'grad_norm': 0.7836953401565552, 'learning_rate': 0.00014349238112421024, 'epoch': 0.39}
{'loss': 0.4465, 'grad_norm': 0.8382024765014648, 'learning_rate': 0.000141050730179282, 'epoch': 0.4}
{'loss': 0.6015, 'grad_norm': 1.106084942817688, 'learning_rate': 0.0001385792805384625, 'epoch': 0.41}


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{'loss': 0.4189, 'grad_norm': 0.9363518953323364, 'learning_rate': 0.00011529740024212565, 'epoch': 0.48}
{'loss': 0.587, 'grad_norm': 1.1184571981430054, 'learning_rate': 0.00011262954860269399, 'epoch': 0.49}


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{'loss': 0.4495, 'grad_norm': 0.9925371408462524, 'learning_rate': 8.844026771946147e-05, 'epoch': 0.56}
{'loss': 0.5536, 'grad_norm': 1.0751277208328247, 'learning_rate': 8.57685161726715e-05, 'epoch': 0.57}


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{'loss': 0.364, 'grad_norm': 0.7982805371284485, 'learning_rate': 6.492739104639727e-05, 'epoch': 0.64}
{'loss': 0.4519, 'grad_norm': 0.7055819034576416, 'learning_rate': 6.24172417885762e-05, 'epoch': 0.64}
{'loss': 0.369, 'grad_norm': 1.0085915327072144, 'learning_rate': 5.9934373828257106e-05, 'epoch': 0.65}


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 81%|████████▏ | 500/614 [3:04:24<42:38, 22.44s/it]{'eval_loss': 0.29790911078453064, 'eval_runtime': 120.1996, 'eval_samples_per_second': 2.097, 'eval_steps_per_second': 1.048, 'epoch': 0.73}
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{'loss': 0.4591, 'grad_norm': 0.9375382661819458, 'learning_rate': 2.184085596336011e-05, 'epoch': 0.8}
{'loss': 0.4813, 'grad_norm': 1.062622308731079, 'learning_rate': 2.0188727106162874e-05, 'epoch': 0.81}
{'loss': 0.4651, 'grad_norm': 0.8038843870162964, 'learning_rate': 1.8594533195293427e-05, 'epoch': 0.81}


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{'loss': 0.4135, 'grad_norm': 0.970380961894989, 'learning_rate': 8.056301829242784e-06, 'epoch': 0.88}
{'loss': 0.2724, 'grad_norm': 0.5573286414146423, 'learning_rate': 7.030288372993066e-06, 'epoch': 0.89}
{'loss': 0.4606, 'grad_norm': 0.9565848112106323, 'learning_rate': 6.0717615644051206e-06, 'epoch': 0.9}


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 98%|█████████▊| 600/614 [3:41:53<04:29, 19.25s/it]{'eval_loss': 0.2927910387516022, 'eval_runtime': 120.4502, 'eval_samples_per_second': 2.092, 'eval_steps_per_second': 1.046, 'epoch': 0.9}
{'loss': 0.4854, 'grad_norm': 0.7690985798835754, 'learning_rate': 5.181417197408734e-06, 'epoch': 0.9}
{'loss': 0.425, 'grad_norm': 0.7944011688232422, 'learning_rate': 4.359901572347758e-06, 'epoch': 0.91}
{'loss': 0.5035, 'grad_norm': 1.0082036256790161, 'learning_rate': 3.607811026831176e-06, 'epoch': 0.92}
{'loss': 0.3532, 'grad_norm': 0.9810898303985596, 'learning_rate': 2.9256915028519573e-06, 'epoch': 0.93}
{'loss': 0.4653, 'grad_norm': 0.8743959069252014, 'learning_rate': 2.3140381504882737e-06, 'epoch': 0.94}
{'loss': 0.351, 'grad_norm': 0.8244614601135254, 'learning_rate': 1.7732949684743594e-06, 'epoch': 0.94}
{'loss': 0.4347, 'grad_norm': 1.2245746850967407, 'learning_rate': 1.303854481902067e-06, 'epoch': 0.95}
{'loss': 0.4403, 'grad_norm': 0.9963445663452148, 'learning_rate': 9.060574572873237e-07, 'epoch': 0.96}
{'loss': 0.4273, 'grad_norm': 1.018866777420044, 'learning_rate': 5.801926552078563e-07, 'epoch': 0.97}
{'loss': 0.4367, 'grad_norm': 1.0799201726913452, 'learning_rate': 3.264966206921294e-07, 'epoch': 0.98}


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{'eval_loss': 0.2925775945186615, 'eval_runtime': 120.683, 'eval_samples_per_second': 2.088, 'eval_steps_per_second': 1.044, 'epoch': 0.98}
{'loss': 0.478, 'grad_norm': 1.1354663372039795, 'learning_rate': 1.451535115114866e-07, 'epoch': 0.99}
{'loss': 0.3683, 'grad_norm': 0.8019553422927856, 'learning_rate': 3.629496450011649e-08, 'epoch': 0.99}
{'train_runtime': 13736.174, 'train_samples_per_second': 0.715, 'train_steps_per_second': 0.045, 'train_loss': 0.5536840389139878, 'epoch': 1.0}
wandb artifact logging skipped (WANDB_SKIP_ARTIFACT=1)
wandb: 
wandb: Run history:
wandb:               eval/loss █▄▃▂▂▂▂▁▁▁▁▁
wandb:            eval/runtime █▁▁▁▁▁▁▁▁▁▁▁
wandb: eval/samples_per_second ▁███████████
wandb:   eval/steps_per_second ▁███████████
wandb:             train/epoch ▁▁▁▂▂▂▂▂▂▂▃▃▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▆▆▆▇▇▇▇▇▇███
wandb:       train/global_step ▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇▇▇██
wandb:         train/grad_norm █▅▂▃▂▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:     train/learning_rate ▃▄▅███▇▇▇▇▇▇▆▆▆▆▆▅▅▅▄▄▄▄▃▃▃▃▃▂▂▂▂▂▂▁▁▁▁▁
wandb:              train/loss ██▂▂▂▂▂▂▁▁▂▂▂▂▁▁▁▁▁▂▁▁▁▂▁▁▂▁▁▁▂▂▁▁▁▁▁▁▁▁
wandb: 
wandb: Run summary:
wandb:               eval/loss 0.29258
wandb:            eval/runtime 120.683
wandb: eval/samples_per_second 2.088
wandb:   eval/steps_per_second 1.044
wandb:              total_flos 2.508719122176676e+18
wandb:             train/epoch 1
wandb:       train/global_step 614
wandb:         train/grad_norm 0.80196
wandb:     train/learning_rate 0.0
wandb:              train/loss 0.3683
wandb:                      +4 ...
wandb: 
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /workspace/anchor/qwen-compassion/wandb/wandb/offline-run-20261011_232644-035fmlpl
wandb: Find logs at: /workspace/anchor/qwen-compassion/wandb/wandb/offline-run-20261011_232644-035fmlpl/logs
done - final adapter at /workspace/anchor/qwen-compassion/train/final
merging and pushing 16-bit model to CompassioninMachineLearning/qwen-compassion-part1-build ...

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Found HuggingFace hub cache directory: /workspace/hf_cache/hub
Checking cache directory for required files...

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Successfully copied all 1 files from cache to `CompassioninMachineLearning/qwen-compassion-part1-build`
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.

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Unsloth: Merging weights into 16bit: 100%|██████████| 1/1 [03:44<00:00, 224.77s/it]
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Unsloth: Merge process complete. Saved to `/workspace/repo/CompassioninMachineLearning/qwen-compassion-part1-build`
pushed merged 16-bit model to https://huggingface.co/CompassioninMachineLearning/qwen-compassion-part1-build