| 🦥 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 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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| 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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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} |
| {'loss': 0.8596, 'grad_norm': 1.2156940698623657, 'learning_rate': 0.00019996933678400946, 'epoch': 0.06} |
| {'loss': 0.8833, 'grad_norm': 1.3200056552886963, 'learning_rate': 0.00019990989662046818, 'epoch': 0.06} |
| {'loss': 0.8131, 'grad_norm': 1.5341733694076538, 'learning_rate': 0.00019981919487090972, 'epoch': 0.07} |
| {'loss': 0.6417, 'grad_norm': 1.1513314247131348, 'learning_rate': 0.00019969725991571128, 'epoch': 0.07} |
| {'loss': 0.8695, 'grad_norm': 0.8719304800033569, 'learning_rate': 0.00019954412990805107, 'epoch': 0.08} |
| {'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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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': 0.533, 'grad_norm': 0.6746335625648499, 'learning_rate': 0.00019155487948487748, 'epoch': 0.18} |
| {'loss': 0.7409, 'grad_norm': 0.766614556312561, 'learning_rate': 0.0001908291231669019, 'epoch': 0.18} |
| {'loss': 0.4672, 'grad_norm': 0.5589352250099182, 'learning_rate': 0.00019007494661686935, 'epoch': 0.19} |
| {'loss': 0.5064, 'grad_norm': 0.59783935546875, 'learning_rate': 0.00018929258581495685, 'epoch': 0.19} |
| {'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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| {'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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| {'loss': 0.5007, 'grad_norm': 0.5472406148910522, 'learning_rate': 0.00014815056369154038, 'epoch': 0.37} |
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[A
37%|███▋ | 350/935 [2:18:14<2:28:45, 15.26s/it] |
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100%|██████████| 192/192 [01:04<00:00, 3.40it/s][A |
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[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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