| `rope_scaling`'s beta_fast field must be a float, got 32 |
| `rope_scaling`'s beta_slow field must be a float, got 1 |
| `rope_scaling`'s beta_fast field must be a float, got 32 |
| `rope_scaling`'s beta_slow field must be a float, got 1 |
| 🦥 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: QLoRA and full finetuning all not selected. Switching to 16bit LoRA. |
|
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| ROPE_GUARD OK train_unsloth load allenai/Olmo-3-1025-7B: olmo3 theta=500000.0 scaling={'rope_type': 'yarn', 'factor': 8.0, 'original_max_position_embeddings': 8192.0, 'beta_fast': 32.0, 'beta_slow': 1.0, 'attention_factor': 1.2079441541679836} |
| 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: 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 4221981, replay 0 (0.0% replay) |
| rows: 14250 raw -> 14138 kept; by source: {'apigen': 4888, 'recovery': 3750, 'xlam': 5000, 'efficiency': 500} |
| [manifest] git=534143b028a9765e904b4d6d1b22205027804f19 data_sha256={'train_olmo-pretrained_replay50.jsonl': '64d5d5af76e7', 'xlam5000_max1call.jsonl': 'a8aae6cf5c36'} |
| wandb: WARNING git root /workspace/anchor/olmo-pretrained/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-pretrained/wandb/wandb/offline-run-20261016_031238-pqlcax1d |
| 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 = 13,784 | Num Epochs = 1 | Total steps = 862 |
| 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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6%|▌ | 50/862 [19:03<5:12:02, 23.06s/it]Unsloth: Will smartly offload gradients to save VRAM! |
| Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass. |
| {'loss': 0.9941, 'grad_norm': 1.8992259502410889, 'learning_rate': 1.8181818181818182e-05, 'epoch': 0.01} |
| {'loss': 0.8727, 'grad_norm': 1.5108100175857544, 'learning_rate': 4.0909090909090915e-05, 'epoch': 0.01} |
| {'loss': 0.7164, 'grad_norm': 1.313894271850586, 'learning_rate': 6.363636363636364e-05, 'epoch': 0.02} |
| {'loss': 0.5324, 'grad_norm': 1.5354955196380615, 'learning_rate': 8.636363636363637e-05, 'epoch': 0.02} |
| {'loss': 0.4492, 'grad_norm': 1.4390828609466553, 'learning_rate': 0.00010909090909090909, 'epoch': 0.03} |
| {'loss': 0.4172, 'grad_norm': 1.8266173601150513, 'learning_rate': 0.0001318181818181818, 'epoch': 0.03} |
| {'loss': 0.3966, 'grad_norm': 1.5203430652618408, 'learning_rate': 0.00015454545454545454, 'epoch': 0.04} |
| {'loss': 0.3584, 'grad_norm': 1.6018481254577637, 'learning_rate': 0.00017727272727272728, 'epoch': 0.05} |
| {'loss': 0.3571, 'grad_norm': 1.3058902025222778, 'learning_rate': 0.0002, 'epoch': 0.05} |
| {'loss': 0.2975, 'grad_norm': 1.388102650642395, 'learning_rate': 0.00019998156301911284, 'epoch': 0.06} |
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12%|█▏ | 100/862 [38:33<4:09:12, 19.62s/it]{'eval_loss': 0.2423536628484726, 'eval_runtime': 152.5048, 'eval_samples_per_second': 2.321, 'eval_steps_per_second': 1.161, 'epoch': 0.06} |
| {'loss': 0.3098, 'grad_norm': 1.3211257457733154, 'learning_rate': 0.0001999262588748966, 'epoch': 0.06} |
| {'loss': 0.27, 'grad_norm': 1.0737800598144531, 'learning_rate': 0.00019983410796018032, 'epoch': 0.07} |
| {'loss': 0.2597, 'grad_norm': 1.062996745109558, 'learning_rate': 0.00019970514425465705, 'epoch': 0.08} |
| {'loss': 0.2213, 'grad_norm': 0.8997095227241516, 'learning_rate': 0.00019953941531235424, 'epoch': 0.08} |
| {'loss': 0.2616, 'grad_norm': 0.6930145025253296, 'learning_rate': 0.00019933698224409876, 'epoch': 0.09} |
| {'loss': 0.2218, 'grad_norm': 0.5634636282920837, 'learning_rate': 0.0001990979196949828, 'epoch': 0.09} |
| {'loss': 0.238, 'grad_norm': 0.545459508895874, 'learning_rate': 0.00019882231581683937, 'epoch': 0.1} |
| {'loss': 0.2145, 'grad_norm': 0.4912775456905365, 'learning_rate': 0.0001985102722357371, 'epoch': 0.1} |
| {'loss': 0.2336, 'grad_norm': 0.4108012914657593, 'learning_rate': 0.0001981619040145068, 'epoch': 0.11} |
| {'loss': 0.1963, 'grad_norm': 0.521858274936676, 'learning_rate': 0.00019777733961031326, 'epoch': 0.12} |
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[A
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|
[Awandb: WARNING URL not available in offline run |
| `rope_scaling`'s beta_fast field must be a float, got 32 |
| `rope_scaling`'s beta_slow field must be a float, got 1 |
|
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| {'loss': 0.2433, 'grad_norm': 0.5323580503463745, 'learning_rate': 0.00019735672082728782, 'epoch': 0.12} |
| {'loss': 0.2063, 'grad_norm': 0.5639608502388, 'learning_rate': 0.0001969002027642398, 'epoch': 0.13} |
| {'loss': 0.2208, 'grad_norm': 0.583233654499054, 'learning_rate': 0.00019640795375746518, 'epoch': 0.13} |
| {'loss': 0.204, 'grad_norm': 0.5018272399902344, 'learning_rate': 0.00019588015531867464, 'epoch': 0.14} |
| {'loss': 0.2155, 'grad_norm': 0.48238405585289, 'learning_rate': 0.00019531700206806274, 'epoch': 0.15} |
| {'loss': 0.208, 'grad_norm': 0.4539037048816681, 'learning_rate': 0.00019471870166254377, 'epoch': 0.15} |
| {'loss': 0.2292, 'grad_norm': 0.5225255489349365, 'learning_rate': 0.00019408547471918061, 'epoch': 0.16} |
| {'loss': 0.2074, 'grad_norm': 0.4645092785358429, 'learning_rate': 0.00019341755473383432, 'epoch': 0.16} |
| {'loss': 0.2301, 'grad_norm': 0.47805657982826233, 'learning_rate': 0.00019271518799506492, 'epoch': 0.17} |
| {'loss': 0.1916, 'grad_norm': 0.43033567070961, 'learning_rate': 0.00019197863349331522, 'epoch': 0.17} |
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23%|██▎ | 200/862 [1:11:36<2:56:00, 15.95s/it]{'eval_loss': 0.1569332629442215, 'eval_runtime': 57.9274, 'eval_samples_per_second': 6.111, 'eval_steps_per_second': 3.056, 'epoch': 0.17} |
| {'loss': 0.2054, 'grad_norm': 0.5715239644050598, 'learning_rate': 0.00019120816282541063, 'epoch': 0.18} |
| {'loss': 0.2156, 'grad_norm': 0.5529009699821472, 'learning_rate': 0.00019040406009441073, 'epoch': 0.19} |
| {'loss': 0.227, 'grad_norm': 0.5217124819755554, 'learning_rate': 0.00018956662180484913, 'epoch': 0.19} |
| {'loss': 0.2191, 'grad_norm': 0.4713853895664215, 'learning_rate': 0.00018869615675340068, 'epoch': 0.2} |
| {'loss': 0.2052, 'grad_norm': 0.44989559054374695, 'learning_rate': 0.00018779298591501564, 'epoch': 0.2} |
| {'loss': 0.2009, 'grad_norm': 0.5454024076461792, 'learning_rate': 0.00018685744232456374, 'epoch': 0.21} |
| {'loss': 0.175, 'grad_norm': 0.5243939161300659, 'learning_rate': 0.0001858898709540309, 'epoch': 0.21} |
| {'loss': 0.1861, 'grad_norm': 0.4877641499042511, 'learning_rate': 0.0001848906285853145, 'epoch': 0.22} |
| {'loss': 0.1775, 'grad_norm': 0.45391249656677246, 'learning_rate': 0.0001838600836786635, 'epoch': 0.23} |
| {'loss': 0.1791, 'grad_norm': 0.49695494771003723, 'learning_rate': 0.0001827986162368129, 'epoch': 0.23} |
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[A
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|
[Awandb: WARNING URL not available in offline run |
| `rope_scaling`'s beta_fast field must be a float, got 32 |
| `rope_scaling`'s beta_slow field must be a float, got 1 |
|
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| {'loss': 0.2095, 'grad_norm': 0.3668426275253296, 'learning_rate': 0.0001817066176648615, 'epoch': 0.24} |
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35%|███▍ | 300/862 [1:42:09<2:31:03, 16.13s/it]{'eval_loss': 0.1410292237997055, 'eval_runtime': 57.9112, 'eval_samples_per_second': 6.113, 'eval_steps_per_second': 3.056, 'epoch': 0.29} |
| {'loss': 0.1774, 'grad_norm': 0.41748952865600586, 'learning_rate': 0.00016920138038595216, 'epoch': 0.3} |
| {'loss': 0.207, 'grad_norm': 0.4169933795928955, 'learning_rate': 0.0001678024805832284, 'epoch': 0.3} |
| {'loss': 0.1492, 'grad_norm': 0.3983752727508545, 'learning_rate': 0.0001663785793197323, 'epoch': 0.31} |
| {'loss': 0.1154, 'grad_norm': 0.3772595524787903, 'learning_rate': 0.00016493020164427152, 'epoch': 0.31} |
| {'loss': 0.177, 'grad_norm': 0.3978261947631836, 'learning_rate': 0.00016345788163107646, 'epoch': 0.32} |
| {'loss': 0.2136, 'grad_norm': 0.41389644145965576, 'learning_rate': 0.0001619621621828659, 'epoch': 0.33} |
| {'loss': 0.1915, 'grad_norm': 0.42459338903427124, 'learning_rate': 0.0001604435948306575, 'epoch': 0.33} |
| {'loss': 0.1747, 'grad_norm': 0.41835540533065796, 'learning_rate': 0.0001589027395303962, 'epoch': 0.34} |
| {'loss': 0.1698, 'grad_norm': 0.5012316703796387, 'learning_rate': 0.0001573401644564764, 'epoch': 0.34} |
| {'loss': 0.1693, 'grad_norm': 0.43907007575035095, 'learning_rate': 0.00015575644579223362, 'epoch': 0.35} |
|
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[A
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|
[Awandb: WARNING URL not available in offline run |
| `rope_scaling`'s beta_fast field must be a float, got 32 |
| `rope_scaling`'s beta_slow field must be a float, got 1 |
|
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37%|███▋ | 323/862 [1:51:27<3:24:06, 22.72s/it]Terminated |
|
|
