🦥 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 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: 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`.
Generating train split: 0 examples [00:00, ? examples/s]Generating train split: 5000 examples [00:00, 8091.85 examples/s]Generating train split: 5000 examples [00:00, 7864.64 examples/s]
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supervised tokens: tool 4521017, replay 11007513 (70.9% replay)
replay capped to 25% of supervised tokens: kept 1506480 replay tokens, 1265 replay rows
rows: 23500 raw -> 15339 kept; by source: {'apigen': 4824, 'xlam': 5000, 'recovery': 3750, 'replay:urban_12738_cleaned': 1265, 'efficiency': 500}
[manifest] git=fedc86c2f311602b2ebdb86e609383ceee4786c0 data_sha256={'train_olmo-urban_replay50.jsonl': '5efef55a9b7c', 'xlam5000_max1call.jsonl': 'a8aae6cf5c36'}
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-urban/wandb/wandb/offline-run-20261012_205325-hbpaapsf
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 = 14,955 | 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': 7.0706, 'grad_norm': 11.332925796508789, 'learning_rate': 1.7021276595744682e-05, 'epoch': 0.01}
{'loss': 6.4977, 'grad_norm': 6.2103447914123535, 'learning_rate': 3.829787234042553e-05, 'epoch': 0.01}
{'loss': 5.6125, 'grad_norm': 4.533832550048828, 'learning_rate': 5.9574468085106384e-05, 'epoch': 0.02}
{'loss': 4.7692, 'grad_norm': 3.615572214126587, 'learning_rate': 8.085106382978723e-05, 'epoch': 0.02}
{'loss': 3.9879, 'grad_norm': 2.759181022644043, 'learning_rate': 0.00010212765957446809, 'epoch': 0.03}
{'loss': 3.339, 'grad_norm': 7.817636966705322, 'learning_rate': 0.00012340425531914893, 'epoch': 0.03}
{'loss': 2.782, 'grad_norm': 1.8590707778930664, 'learning_rate': 0.0001446808510638298, 'epoch': 0.04}
{'loss': 2.0399, 'grad_norm': 1.6293493509292603, 'learning_rate': 0.00016595744680851065, 'epoch': 0.04}
{'loss': 1.7934, 'grad_norm': 1.9784948825836182, 'learning_rate': 0.0001872340425531915, 'epoch': 0.05}
{'loss': 1.3086, 'grad_norm': 1.2116178274154663, 'learning_rate': 0.00019999749676283775, 'epoch': 0.05}
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[A 5%|▌ | 51/935 [29:06<23:52:21, 97.22s/it] 6%|▌ | 52/935 [29:28<18:18:08, 74.62s/it] 6%|▌ | 53/935 [29:55<14:44:16, 60.15s/it] 6%|▌ | 54/935 [30:25<12:31:46, 51.20s/it] 6%|▌ | 55/935 [30:50<10:34:37, 43.27s/it] 6%|▌ | 55/935 [30:50<10:34:37, 43.27s/it] 6%|▌ | 56/935 [31:21<9:41:08, 39.67s/it] 6%|▌ | 57/935 [31:45<8:33:53, 35.12s/it] 6%|▌ | 58/935 [32:13<7:58:40, 32.75s/it] 6%|▋ | 59/935 [32:33<7:03:17, 28.99s/it] 6%|▋ | 60/935 [32:55<6:30:56, 26.81s/it] 6%|▋ | 60/935 [32:55<6:30:56, 26.81s/it] 7%|▋ | 61/935 [33:25<6:44:44, 27.79s/it] 7%|▋ | 62/935 [33:49<6:30:42, 26.85s/it] 7%|▋ | 63/935 [34:06<5:45:52, 23.80s/it] 7%|▋ | 64/935 [34:16<4:44:07, 19.57s/it] 7%|▋ | 65/935 [34:45<5:25:16, 22.43s/it] 7%|▋ | 65/935 [34:45<5:25:16, 22.43s/it] 7%|▋ | 66/935 [35:14<5:53:16, 24.39s/it] 7%|▋ | 67/935 [35:43<6:15:49, 25.98s/it] 7%|▋ | 68/935 [36:12<6:27:31, 26.82s/it] 7%|▋ | 69/935 [36:41<6:35:44, 27.42s/it] 7%|▋ | 70/935 [37:13<6:53:28, 28.68s/it] 7%|▋ | 70/935 [37:13<6:53:28, 28.68s/it] 8%|▊ | 71/935 [37:33<6:18:26, 26.28s/it] 8%|▊ | 72/935 [38:04<6:34:52, 27.45s/it] 8%|▊ | 73/935 [38:26<6:13:52, 26.02s/it] 8%|▊ | 74/935 [38:59<6:42:16, 28.03s/it] 8%|▊ | 75/935 [39:22<6:20:54, 26.57s/it] 8%|▊ | 75/935 [39:22<6:20:54, 26.57s/it] 8%|▊ | 76/935 [39:45<6:05:40, 25.54s/it] 8%|▊ | 77/935 [40:14<6:18:44, 26.49s/it] 8%|▊ | 78/935 [40:45<6:35:42, 27.70s/it] 8%|▊ | 79/935 [41:09<6:19:45, 26.62s/it] 9%|▊ | 80/935 [41:28<5:49:36, 24.53s/it] 9%|▊ | 80/935 [41:28<5:49:36, 24.53s/it] 9%|▊ | 81/935 [41:56<6:03:45, 25.56s/it] 9%|▉ | 82/935 [42:24<6:12:25, 26.20s/it] 9%|▉ | 83/935 [42:56<6:37:47, 28.01s/it] 9%|▉ | 84/935 [43:23<6:33:55, 27.77s/it] 9%|▉ | 85/935 [43:56<6:54:50, 29.28s/it] 9%|▉ | 85/935 [43:56<6:54:50, 29.28s/it] 9%|▉ | 86/935 [44:21<6:33:39, 27.82s/it] 9%|▉ | 87/935 [44:49<6:37:14, 28.11s/it] 9%|▉ | 88/935 [45:12<6:14:34, 26.53s/it] 10%|▉ | 89/935 [45:39<6:16:28, 26.70s/it] 10%|▉ | 90/935 [46:07<6:18:34, 26.88s/it] 10%|▉ | 90/935 [46:07<6:18:34, 26.88s/it] 10%|▉ | 91/935 [46:34<6:18:13, 26.89s/it] 10%|▉ | 92/935 [47:00<6:17:14, 26.85s/it] 10%|▉ | 93/935 [47:20<5:46:16, 24.68s/it] 10%|█ | 94/935 [47:46<5:51:47, 25.10s/it] 10%|█ | 95/935 [48:12<5:56:56, 25.50s/it] 10%|█ | 95/935 [48:12<5:56:56, 25.50s/it] 10%|█ | 96/935 [48:48<6:37:20, 28.42s/it] 10%|█ | 97/935 [49:14<6:26:13, 27.65s/it] 10%|█ | 98/935 [49:36<6:05:03, 26.17s/it] 11%|█ | 99/935 [49:59<5:49:07, 25.06s/it] 11%|█ | 100/935 [50:21<5:35:38, 24.12s/it] 11%|█ | 100/935 [50:21<5:35:38, 24.12s/it]{'eval_loss': 0.8905913829803467, 'eval_runtime': 230.3688, 'eval_samples_per_second': 1.667, 'eval_steps_per_second': 0.833, 'epoch': 0.05}
{'loss': 1.1475, 'grad_norm': 1.6632639169692993, 'learning_rate': 0.00019996933678400946, 'epoch': 0.06}
{'loss': 0.9136, 'grad_norm': 1.2125474214553833, 'learning_rate': 0.00019990989662046818, 'epoch': 0.06}
{'loss': 0.8924, 'grad_norm': 1.0796895027160645, 'learning_rate': 0.00019981919487090972, 'epoch': 0.07}
{'loss': 0.8299, 'grad_norm': 0.9552472829818726, 'learning_rate': 0.00019969725991571128, 'epoch': 0.07}
{'loss': 0.7001, 'grad_norm': 0.7944055199623108, 'learning_rate': 0.00019954412990805107, 'epoch': 0.08}
{'loss': 0.8231, 'grad_norm': 0.7805637121200562, 'learning_rate': 0.0001993598527619703, 'epoch': 0.09}
{'loss': 0.8797, 'grad_norm': 0.6794283986091614, 'learning_rate': 0.00019914448613738106, 'epoch': 0.09}
{'loss': 0.6609, 'grad_norm': 0.6624935269355774, 'learning_rate': 0.00019889809742202455, 'epoch': 0.1}
{'loss': 0.8429, 'grad_norm': 0.8070583939552307, 'learning_rate': 0.0001986207637103857, 'epoch': 0.1}
{'loss': 0.6952, 'grad_norm': 0.7406468391418457, 'learning_rate': 0.00019831257177957044, 'epoch': 0.11}
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[Awandb: WARNING URL not available in offline run
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{'loss': 0.4831, 'grad_norm': 0.672978937625885, 'learning_rate': 0.00019797361806215332, 'epoch': 0.11}
{'loss': 0.7488, 'grad_norm': 0.6432732939720154, 'learning_rate': 0.000197604008616004, 'epoch': 0.12}
{'loss': 0.6459, 'grad_norm': 0.8533371686935425, 'learning_rate': 0.00019720385909110198, 'epoch': 0.12}
{'loss': 0.6682, 'grad_norm': 0.7265816926956177, 'learning_rate': 0.0001967732946933499, 'epoch': 0.13}
{'loss': 0.6458, 'grad_norm': 0.7138218879699707, 'learning_rate': 0.00019631245014539698, 'epoch': 0.13}
{'loss': 0.7498, 'grad_norm': 0.681537389755249, 'learning_rate': 0.0001958214696444846, 'epoch': 0.14}
{'loss': 0.5161, 'grad_norm': 0.7001609206199646, 'learning_rate': 0.0001953005068173272, 'epoch': 0.14}
{'loss': 0.6795, 'grad_norm': 0.6696146130561829, 'learning_rate': 0.00019474972467204297, 'epoch': 0.15}
{'loss': 0.6418, 'grad_norm': 0.781279981136322, 'learning_rate': 0.00019416929554714888, 'epoch': 0.16}
{'loss': 0.5244, 'grad_norm': 0.5625928044319153, 'learning_rate': 0.0001935594010576362, 'epoch': 0.16}
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[A 16%|█▌ | 151/935 [1:20:51<13:18:23, 61.10s/it] 16%|█▋ | 152/935 [1:21:12<10:39:16, 48.99s/it] 16%|█▋ | 153/935 [1:21:39<9:13:33, 42.47s/it] 16%|█▋ | 154/935 [1:21:55<7:30:43, 34.63s/it] 17%|█▋ | 155/935 [1:22:16<6:36:17, 30.48s/it] 17%|█▋ | 155/935 [1:22:16<6:36:17, 30.48s/it] 17%|█▋ | 156/935 [1:22:40<6:09:04, 28.43s/it] 17%|█▋ | 157/935 [1:23:04<5:50:20, 27.02s/it] 17%|█▋ | 158/935 [1:23:30<5:46:32, 26.76s/it] 17%|█▋ | 159/935 [1:23:51<5:25:55, 25.20s/it] 17%|█▋ | 160/935 [1:24:13<5:13:54, 24.30s/it] 17%|█▋ | 160/935 [1:24:13<5:13:54, 24.30s/it] 17%|█▋ | 161/935 [1:24:37<5:09:30, 23.99s/it] 17%|█▋ | 162/935 [1:25:01<5:09:08, 24.00s/it] 17%|█▋ | 163/935 [1:25:25<5:08:35, 23.98s/it] 18%|█▊ | 164/935 [1:25:55<5:33:04, 25.92s/it] 18%|█▊ | 165/935 [1:26:22<5:36:39, 26.23s/it] 18%|█▊ | 165/935 [1:26:22<5:36:39, 26.23s/it] 18%|█▊ | 166/935 [1:26:41<5:06:37, 23.92s/it] 18%|█▊ | 167/935 [1:27:07<5:14:45, 24.59s/it] 18%|█▊ | 168/935 [1:27:35<5:27:06, 25.59s/it] 18%|█▊ | 169/935 [1:27:56<5:10:28, 24.32s/it] 18%|█▊ | 170/935 [1:28:13<4:40:19, 21.99s/it] 18%|█▊ | 170/935 [1:28:13<4:40:19, 21.99s/it] 18%|█▊ | 171/935 [1:28:43<5:10:35, 24.39s/it] 18%|█▊ | 172/935 [1:29:05<5:01:07, 23.68s/it] 19%|█▊ | 173/935 [1:29:34<5:23:35, 25.48s/it] 19%|█▊ | 174/935 [1:29:55<5:04:06, 23.98s/it] 19%|█▊ | 175/935 [1:30:24<5:22:37, 25.47s/it] 19%|█▊ | 175/935 [1:30:24<5:22:37, 25.47s/it] 19%|█▉ | 176/935 [1:30:49<5:22:52, 25.52s/it] 19%|█▉ | 177/935 [1:31:23<5:55:10, 28.11s/it] 19%|█▉ | 178/935 [1:31:41<5:12:57, 24.81s/it] 19%|█▉ | 179/935 [1:32:00<4:53:44, 23.31s/it] 19%|█▉ | 180/935 [1:32:18<4:32:14, 21.63s/it] 19%|█▉ | 180/935 [1:32:18<4:32:14, 21.63s/it] 19%|█▉ | 181/935 [1:32:50<5:08:50, 24.58s/it] 19%|█▉ | 182/935 [1:33:14<5:06:04, 24.39s/it] 20%|█▉ | 183/935 [1:33:31<4:39:45, 22.32s/it] 20%|█▉ | 184/935 [1:33:59<5:00:48, 24.03s/it] 20%|█▉ | 185/935 [1:34:23<5:00:24, 24.03s/it] 20%|█▉ | 185/935 [1:34:23<5:00:24, 24.03s/it] 20%|█▉ | 186/935 [1:34:46<4:54:47, 23.61s/it] 20%|██ | 187/935 [1:35:16<5:18:38, 25.56s/it] 20%|██ | 188/935 [1:35:38<5:04:23, 24.45s/it] 20%|██ | 189/935 [1:36:03<5:06:04, 24.62s/it] 20%|██ | 190/935 [1:36:18<4:31:21, 21.85s/it] 20%|██ | 190/935 [1:36:18<4:31:21, 21.85s/it] 20%|██ | 191/935 [1:36:46<4:54:25, 23.74s/it] 21%|██ | 192/935 [1:37:12<5:02:36, 24.44s/it] 21%|██ | 193/935 [1:37:34<4:50:21, 23.48s/it] 21%|██ | 194/935 [1:37:58<4:53:07, 23.73s/it] 21%|██ | 195/935 [1:38:17<4:36:16, 22.40s/it] 21%|██ | 195/935 [1:38:17<4:36:16, 22.40s/it] 21%|██ | 196/935 [1:38:38<4:31:26, 22.04s/it] 21%|██ | 197/935 [1:39:11<5:12:02, 25.37s/it] 21%|██ | 198/935 [1:39:35<5:05:15, 24.85s/it] 21%|██▏ | 199/935 [1:40:02<5:10:40, 25.33s/it] 21%|██▏ | 200/935 [1:40:33<5:33:53, 27.26s/it] 21%|██▏ | 200/935 [1:40:33<5:33:53, 27.26s/it]{'eval_loss': 0.4266001880168915, 'eval_runtime': 111.8623, 'eval_samples_per_second': 3.433, 'eval_steps_per_second': 1.716, 'epoch': 0.16}
{'loss': 0.7282, 'grad_norm': 0.5409214496612549, 'learning_rate': 0.000192920232038144, 'epoch': 0.17}
{'loss': 0.8597, 'grad_norm': 0.6080542206764221, 'learning_rate': 0.0001922519884832469, 'epoch': 0.17}
{'loss': 0.4898, 'grad_norm': 0.650615930557251, 'learning_rate': 0.00019155487948487748, 'epoch': 0.18}
{'loss': 0.6313, 'grad_norm': 0.5822589993476868, 'learning_rate': 0.0001908291231669019, 'epoch': 0.18}
{'loss': 0.5673, 'grad_norm': 0.5433451533317566, 'learning_rate': 0.00019007494661686935, 'epoch': 0.19}
{'loss': 0.5888, 'grad_norm': 0.6697724461555481, 'learning_rate': 0.00018929258581495685, 'epoch': 0.19}
{'loss': 0.5925, 'grad_norm': 0.7195097208023071, 'learning_rate': 0.0001884822855601316, 'epoch': 0.2}
{'loss': 0.6258, 'grad_norm': 0.704847514629364, 'learning_rate': 0.00018764429939355392, 'epoch': 0.2}
{'loss': 0.6023, 'grad_norm': 0.7211595177650452, 'learning_rate': 0.00018677888951924474, 'epoch': 0.21}
{'loss': 0.4632, 'grad_norm': 0.5733616948127747, 'learning_rate': 0.00018588632672204264, 'epoch': 0.21}
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[Awandb: WARNING URL not available in offline run
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{'loss': 0.541, 'grad_norm': 0.6652664542198181, 'learning_rate': 0.00017890261283540562, 'epoch': 0.25}
{'loss': 0.4365, 'grad_norm': 0.4666398763656616, 'learning_rate': 0.00017780357543184397, 'epoch': 0.26}
{'loss': 0.598, 'grad_norm': 0.5831547975540161, 'learning_rate': 0.0001766801934613467, 'epoch': 0.26}
{'loss': 0.5548, 'grad_norm': 0.5607553124427795, 'learning_rate': 0.00017553281842765169, 'epoch': 0.27}
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[A 27%|██▋ | 251/935 [2:12:52<10:35:28, 55.74s/it] 27%|██▋ | 252/935 [2:13:19<8:54:59, 47.00s/it] 27%|██▋ | 253/935 [2:13:36<7:13:36, 38.15s/it] 27%|██▋ | 254/935 [2:14:02<6:30:16, 34.38s/it] 27%|██▋ | 255/935 [2:14:20<5:34:48, 29.54s/it] 27%|██▋ | 255/935 [2:14:20<5:34:48, 29.54s/it] 27%|██▋ | 256/935 [2:14:38<4:53:23, 25.93s/it] 27%|██▋ | 257/935 [2:15:03<4:52:18, 25.87s/it] 28%|██▊ | 258/935 [2:15:25<4:38:11, 24.66s/it] 28%|██▊ | 259/935 [2:15:41<4:08:19, 22.04s/it] 28%|██▊ | 260/935 [2:16:10<4:30:09, 24.01s/it] 28%|██▊ | 260/935 [2:16:10<4:30:09, 24.01s/it] 28%|██▊ | 261/935 [2:16:41<4:53:54, 26.16s/it] 28%|██▊ | 262/935 [2:17:00<4:29:07, 23.99s/it] 28%|██▊ | 263/935 [2:17:20<4:15:53, 22.85s/it] 28%|██▊ | 264/935 [2:17:42<4:14:02, 22.72s/it] 28%|██▊ | 265/935 [2:18:04<4:09:35, 22.35s/it] 28%|██▊ | 265/935 [2:18:04<4:09:35, 22.35s/it] 28%|██▊ | 266/935 [2:18:29<4:19:40, 23.29s/it] 29%|██▊ | 267/935 [2:18:55<4:28:16, 24.10s/it] 29%|██▊ | 268/935 [2:19:05<3:40:58, 19.88s/it] 29%|██▉ | 269/935 [2:19:13<2:59:32, 16.18s/it] 29%|██▉ | 270/935 [2:19:37<3:26:39, 18.65s/it] 29%|██▉ | 270/935 [2:19:37<3:26:39, 18.65s/it] 29%|██▉ | 271/935 [2:19:56<3:27:47, 18.78s/it] 29%|██▉ | 272/935 [2:20:23<3:52:04, 21.00s/it] 29%|██▉ | 273/935 [2:20:56<4:31:34, 24.61s/it] 29%|██▉ | 274/935 [2:21:17<4:20:21, 23.63s/it] 29%|██▉ | 275/935 [2:21:43<4:28:24, 24.40s/it] 29%|██▉ | 275/935 [2:21:43<4:28:24, 24.40s/it] 30%|██▉ | 276/935 [2:22:05<4:20:41, 23.74s/it] 30%|██▉ | 277/935 [2:22:24<4:05:02, 22.34s/it] 30%|██▉ | 278/935 [2:22:41<3:46:09, 20.65s/it] 30%|██▉ | 279/935 [2:23:00<3:39:21, 20.06s/it] 30%|██▉ | 280/935 [2:23:18<3:33:16, 19.54s/it] 30%|██▉ | 280/935 [2:23:18<3:33:16, 19.54s/it] 30%|███ | 281/935 [2:23:50<4:14:07, 23.31s/it] 30%|███ | 282/935 [2:24:02<3:35:09, 19.77s/it] 30%|███ | 283/935 [2:24:23<3:40:34, 20.30s/it] 30%|███ | 284/935 [2:24:40<3:27:17, 19.11s/it] 30%|███ | 285/935 [2:25:10<4:03:37, 22.49s/it] 30%|███ | 285/935 [2:25:10<4:03:37, 22.49s/it] 31%|███ | 286/935 [2:25:24<3:34:48, 19.86s/it] 31%|███ | 287/935 [2:25:49<3:50:27, 21.34s/it] 31%|███ | 288/935 [2:26:18<4:17:59, 23.93s/it] 31%|███ | 289/935 [2:26:44<4:22:51, 24.41s/it] 31%|███ | 290/935 [2:27:08<4:20:49, 24.26s/it] 31%|███ | 290/935 [2:27:08<4:20:49, 24.26s/it] 31%|███ | 291/935 [2:27:28<4:05:22, 22.86s/it] 31%|███ | 292/935 [2:27:42<3:38:32, 20.39s/it] 31%|███▏ | 293/935 [2:27:57<3:20:02, 18.70s/it] 31%|███▏ | 294/935 [2:28:18<3:27:41, 19.44s/it] 32%|███▏ | 295/935 [2:28:34<3:16:51, 18.46s/it] 32%|███▏ | 295/935 [2:28:34<3:16:51, 18.46s/it] 32%|███▏ | 296/935 [2:29:00<3:39:50, 20.64s/it] 32%|███▏ | 297/935 [2:29:19<3:34:26, 20.17s/it] 32%|███▏ | 298/935 [2:29:42<3:42:28, 20.95s/it] 32%|███▏ | 299/935 [2:30:05<3:49:36, 21.66s/it] 32%|███▏ | 300/935 [2:30:31<4:03:11, 22.98s/it] 32%|███▏ | 300/935 [2:30:31<4:03:11, 22.98s/it]{'eval_loss': 0.3891582489013672, 'eval_runtime': 111.8073, 'eval_samples_per_second': 3.434, 'eval_steps_per_second': 1.717, 'epoch': 0.27}
{'loss': 0.4506, 'grad_norm': 0.5996724367141724, 'learning_rate': 0.00017436180934187308, 'epoch': 0.27}
{'loss': 0.526, 'grad_norm': 0.5043736100196838, 'learning_rate': 0.00017316753261016783, 'epoch': 0.28}
{'loss': 0.3911, 'grad_norm': 0.5816839337348938, 'learning_rate': 0.00017195036191908797, 'epoch': 0.28}
{'loss': 0.6858, 'grad_norm': 0.5987787842750549, 'learning_rate': 0.00017071067811865476, 'epoch': 0.29}
{'loss': 0.5402, 'grad_norm': 0.5184270739555359, 'learning_rate': 0.00016944886910319173, 'epoch': 0.29}
{'loss': 0.6015, 'grad_norm': 0.7627037763595581, 'learning_rate': 0.00016816532968995328, 'epoch': 0.3}
{'loss': 0.4313, 'grad_norm': 0.572108805179596, 'learning_rate': 0.00016686046149558736, 'epoch': 0.3}
{'loss': 0.475, 'grad_norm': 0.5677588582038879, 'learning_rate': 0.0001655346728104704, 'epoch': 0.31}
{'loss': 0.6212, 'grad_norm': 0.5855934619903564, 'learning_rate': 0.0001641883784709541, 'epoch': 0.32}
{'loss': 0.6327, 'grad_norm': 0.5541160106658936, 'learning_rate': 0.00016282199972956425, 'epoch': 0.32}
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[Awandb: WARNING URL not available in offline run
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{'loss': 0.3828, 'grad_norm': 0.48272597789764404, 'learning_rate': 0.00016003070533931657, 'epoch': 0.33}
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{'loss': 0.7714, 'grad_norm': 0.5451855659484863, 'learning_rate': 0.00015273165610577542, 'epoch': 0.36}
{'loss': 0.5547, 'grad_norm': 0.7061304450035095, 'learning_rate': 0.00015122049279531143, 'epoch': 0.36}
{'loss': 0.5838, 'grad_norm': 0.5038750171661377, 'learning_rate': 0.00014969330270576427, 'epoch': 0.37}
{'loss': 0.6429, 'grad_norm': 0.5525210499763489, 'learning_rate': 0.00014815056369154038, 'epoch': 0.37}
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🦥 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 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: 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`.
Generating train split: 0 examples [00:00, ? examples/s]Generating train split: 5000 examples [00:00, 8091.85 examples/s]Generating train split: 5000 examples [00:00, 7864.64 examples/s]
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supervised tokens: tool 4521017, replay 11007513 (70.9% replay)
replay capped to 25% of supervised tokens: kept 1506480 replay tokens, 1265 replay rows
rows: 23500 raw -> 15339 kept; by source: {'apigen': 4824, 'xlam': 5000, 'recovery': 3750, 'replay:urban_12738_cleaned': 1265, 'efficiency': 500}
[manifest] git=fedc86c2f311602b2ebdb86e609383ceee4786c0 data_sha256={'train_olmo-urban_replay50.jsonl': '5efef55a9b7c', 'xlam5000_max1call.jsonl': 'a8aae6cf5c36'}
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-urban/wandb/wandb/offline-run-20261012_205325-hbpaapsf
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 = 14,955 | 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': 7.0706, 'grad_norm': 11.332925796508789, 'learning_rate': 1.7021276595744682e-05, 'epoch': 0.01}
{'loss': 6.4977, 'grad_norm': 6.2103447914123535, 'learning_rate': 3.829787234042553e-05, 'epoch': 0.01}
{'loss': 5.6125, 'grad_norm': 4.533832550048828, 'learning_rate': 5.9574468085106384e-05, 'epoch': 0.02}
{'loss': 4.7692, 'grad_norm': 3.615572214126587, 'learning_rate': 8.085106382978723e-05, 'epoch': 0.02}
{'loss': 3.9879, 'grad_norm': 2.759181022644043, 'learning_rate': 0.00010212765957446809, 'epoch': 0.03}
{'loss': 3.339, 'grad_norm': 7.817636966705322, 'learning_rate': 0.00012340425531914893, 'epoch': 0.03}
{'loss': 2.782, 'grad_norm': 1.8590707778930664, 'learning_rate': 0.0001446808510638298, 'epoch': 0.04}
{'loss': 2.0399, 'grad_norm': 1.6293493509292603, 'learning_rate': 0.00016595744680851065, 'epoch': 0.04}
{'loss': 1.7934, 'grad_norm': 1.9784948825836182, 'learning_rate': 0.0001872340425531915, 'epoch': 0.05}
{'loss': 1.3086, 'grad_norm': 1.2116178274154663, 'learning_rate': 0.00019999749676283775, 'epoch': 0.05}
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