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⚙️ Running in WANDB offline mode
🦥 Unsloth Zoo will now patch everything to make training faster!
==((====))== Unsloth 2026.9.2: Fast Olmo3 patching. Transformers: 4.57.6.
\\ /| NVIDIA H100 80GB HBM3. Num GPUs = 1. Max memory: 79.179 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: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.
Unsloth: Explicit target_modules are constrained by the finetune_(vision|language|attention|mlp) filters; adapters attach only where both select.
Unsloth: Allowing gradients for `base_model.model.model.embed_tokens` since it's in `modules_to_save`.
Unsloth: Allowing gradients for `base_model.model.lm_head` since it's in `modules_to_save`.
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supervised tokens: tool 4521017, replay 11056920 (71.0% replay)
replay capped to 25% of supervised tokens: kept 1506259 replay tokens, 1257 replay rows
rows: 23500 raw -> 15331 kept; by source: {'recovery': 3750, 'xlam': 5000, 'replay:compassion_12185_cleaned': 1257, 'apigen': 4824, 'efficiency': 500}
[manifest] git=f56d0c01687b3b599f164c8b826c3df524c66dbf data_sha256={'train_olmo-compassion_replay50.jsonl': 'b43d87068505', 'xlam5000_max1call.jsonl': 'a8aae6cf5c36'}
wandb: WARNING git root /workspace/anchor/olmo-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/olmo-compassion/wandb/wandb/offline-run-20261013_000822-6sbhg1fn
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 = 14,947 | Num Epochs = 1 | Total steps = 935
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 = 981,385,216 of 8,279,396,352 (11.85% 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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5%|▌ | 50/935 [19:19<5:16:55, 21.49s/it]Unsloth: Will smartly offload gradients to save VRAM!
Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass.
{'loss': 7.033, 'grad_norm': 7.84370756149292, 'learning_rate': 1.7021276595744682e-05, 'epoch': 0.01}
{'loss': 6.5074, 'grad_norm': 6.463988304138184, 'learning_rate': 3.829787234042553e-05, 'epoch': 0.01}
{'loss': 5.5202, 'grad_norm': 3.8663952350616455, 'learning_rate': 5.9574468085106384e-05, 'epoch': 0.02}
{'loss': 4.6312, 'grad_norm': 3.429267168045044, 'learning_rate': 8.085106382978723e-05, 'epoch': 0.02}
{'loss': 3.9047, 'grad_norm': 2.4048895835876465, 'learning_rate': 0.00010212765957446809, 'epoch': 0.03}
{'loss': 3.239, 'grad_norm': 1.6516971588134766, 'learning_rate': 0.00012340425531914893, 'epoch': 0.03}
{'loss': 2.8394, 'grad_norm': 1.9744592905044556, 'learning_rate': 0.0001446808510638298, 'epoch': 0.04}
{'loss': 1.9605, 'grad_norm': 1.9995774030685425, 'learning_rate': 0.00016595744680851065, 'epoch': 0.04}
{'loss': 1.4049, 'grad_norm': 1.9445796012878418, 'learning_rate': 0.0001872340425531915, 'epoch': 0.05}
{'loss': 1.1704, 'grad_norm': 1.7529069185256958, 'learning_rate': 0.00019999749676283775, 'epoch': 0.05}
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[A
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[A
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11%|█ | 100/935 [38:14<4:10:48, 18.02s/it]{'eval_loss': 0.7856047749519348, 'eval_runtime': 169.4008, 'eval_samples_per_second': 2.267, 'eval_steps_per_second': 1.133, 'epoch': 0.05}
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{'loss': 0.6572, 'grad_norm': 0.7839741706848145, 'learning_rate': 0.0001993598527619703, 'epoch': 0.09}
{'loss': 0.782, 'grad_norm': 0.7421998381614685, 'learning_rate': 0.00019914448613738106, 'epoch': 0.09}
{'loss': 0.7823, 'grad_norm': 0.747768759727478, 'learning_rate': 0.00019889809742202455, 'epoch': 0.1}
{'loss': 0.6102, 'grad_norm': 0.7919906973838806, 'learning_rate': 0.0001986207637103857, 'epoch': 0.1}
{'loss': 0.7222, 'grad_norm': 0.6297110915184021, 'learning_rate': 0.00019831257177957044, 'epoch': 0.11}
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[A
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[Awandb: WARNING URL not available in offline run
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16%|█▌ | 150/935 [1:02:19<6:03:38, 27.79s/it]{'eval_loss': 0.42398348450660706, 'eval_runtime': 64.9622, 'eval_samples_per_second': 5.911, 'eval_steps_per_second': 2.956, 'epoch': 0.11}
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{'loss': 0.8686, 'grad_norm': 0.8769603371620178, 'learning_rate': 0.00019416929554714888, 'epoch': 0.16}
{'loss': 0.5971, 'grad_norm': 0.6099892258644104, 'learning_rate': 0.0001935594010576362, 'epoch': 0.16}
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21%|██▏ | 200/935 [1:18:43<3:32:17, 17.33s/it]{'eval_loss': 0.37071776390075684, 'eval_runtime': 136.3832, 'eval_samples_per_second': 2.816, 'eval_steps_per_second': 1.408, 'epoch': 0.16}
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{'loss': 0.6937, 'grad_norm': 0.6479551792144775, 'learning_rate': 0.0001884822855601316, 'epoch': 0.2}
{'loss': 0.5394, 'grad_norm': 0.6843699812889099, 'learning_rate': 0.00018764429939355392, 'epoch': 0.2}
{'loss': 0.7568, 'grad_norm': 0.5675429105758667, 'learning_rate': 0.00018677888951924474, 'epoch': 0.21}
{'loss': 0.4871, 'grad_norm': 0.659488320350647, 'learning_rate': 0.00018588632672204264, 'epoch': 0.21}
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[A
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[A
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32%|███▏ | 300/935 [1:57:03<2:21:59, 13.42s/it]{'eval_loss': 0.3379444181919098, 'eval_runtime': 112.4829, 'eval_samples_per_second': 3.414, 'eval_steps_per_second': 1.707, 'epoch': 0.27}
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{'loss': 0.481, 'grad_norm': 0.4074549674987793, 'learning_rate': 0.0001655346728104704, 'epoch': 0.31}
{'loss': 0.8114, 'grad_norm': 0.6757243275642395, 'learning_rate': 0.0001641883784709541, 'epoch': 0.32}
{'loss': 0.3812, 'grad_norm': 0.5381545424461365, 'learning_rate': 0.00016282199972956425, 'epoch': 0.32}
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37%|███▋ | 350/935 [2:17:09<2:28:45, 15.26s/it]{'eval_loss': 0.33034276962280273, 'eval_runtime': 65.2007, 'eval_samples_per_second': 5.89, 'eval_steps_per_second': 2.945, 'epoch': 0.32}
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93%|█████████▎| 179/192 [01:00<00:06, 2.15it/s][A
94%|█████████▍| 180/192 [01:00<00:05, 2.16it/s][A
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98%|█████████▊| 188/192 [01:03<00:01, 2.85it/s][A
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99%|█████████▉| 191/192 [01:04<00:00, 3.40it/s][A
[A
37%|███▋ | 350/935 [2:18:14<2:28:45, 15.26s/it]
100%|██████████| 192/192 [01:04<00:00, 3.40it/s][A
[A
38%|███▊ | 351/935 [2:18:31<5:43:36, 35.30s/it]
38%|███▊ | 352/935 [2:18:46<4:44:52, 29.32s/it]
38%|███▊ | 353/935 [2:19:03<4:09:15, 25.70s/it]Terminated
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