Hugging Face's logo

ToastyPigeon
/
apertus-12b-instruct-1ep-lora

Text Generation
PEFT
Safetensors
Transformers
apertus
axolotl
lora
conversational
custom_code
Model card Files
xet
Community
apertus-12b-instruct-1ep-lora / debug.log
ToastyPigeon's picture
ToastyPigeon
Upload folder using huggingface_hub
9f9bf08 verified
raw
history blame contribute delete
21.2 kB
 [2025-11-26 03:25:26,747] [INFO] [axolotl.utils.data.sft._load_raw_datasets:320] [PID:64100] Loading raw datasets...
[2025-11-26 03:25:29,268] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:64100] Loading dataset: ToastyPigeon/limarp-augmented-train-last-only with base_type: chat_template and prompt_style: None
[2025-11-26 03:25:29,972] [WARNING] [huggingface_hub.repocard.content:108] [PID:64100] Repo card metadata block was not found. Setting CardData to empty.
[2025-11-26 03:25:30,754] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:64100] Loading dataset: ToastyPigeon/mixed-medical-reasoning-formatted with base_type: chat_template and prompt_style: None
[2025-11-26 03:25:32,584] [WARNING] [huggingface_hub.repocard.content:108] [PID:64100] Repo card metadata block was not found. Setting CardData to empty.
[2025-11-26 03:25:33,613] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:64100] Loading dataset: ToastyPigeon/kimi-stories-instruct with base_type: chat_template and prompt_style: None
[2025-11-26 03:25:35,627] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:64100] Loading dataset: allura-forge/koto-instruct-sft-nothink with base_type: chat_template and prompt_style: None
[2025-11-26 03:25:36,310] [WARNING] [huggingface_hub.repocard.content:108] [PID:64100] Repo card metadata block was not found. Setting CardData to empty.
[2025-11-26 03:25:37,341] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:64100] Loading dataset: ToastyPigeon/SpringDragon-Instruct with base_type: chat_template and prompt_style: None
Tokenizing Prompts (num_proc=24): 0%| | 0/2535 [00:00<?, ? examples/s] Tokenizing Prompts (num_proc=24): 4%|█▌ | 106/2535 [00:03<01:30, 26.77 examples/s] Tokenizing Prompts (num_proc=24): 8%|███ | 212/2535 [00:04<00:46, 49.58 examples/s] Tokenizing Prompts (num_proc=24): 17%|██████ | 423/2535 [00:04<00:17, 124.16 examples/s] Tokenizing Prompts (num_proc=24): 25%|█████████ | 635/2535 [00:05<00:10, 188.35 examples/s] Tokenizing Prompts (num_proc=24): 33%|████████████ | 846/2535 [00:05<00:05, 293.27 examples/s] Tokenizing Prompts (num_proc=24): 38%|█████████████▌ | 951/2535 [00:05<00:05, 292.11 examples/s] Tokenizing Prompts (num_proc=24): 50%|█████████████████▌ | 1268/2535 [00:06<00:02, 496.58 examples/s] Tokenizing Prompts (num_proc=24): 58%|████████████████████▍ | 1479/2535 [00:06<00:01, 548.63 examples/s] Tokenizing Prompts (num_proc=24): 63%|█████████████████████▉ | 1585/2535 [00:06<00:01, 563.28 examples/s] Tokenizing Prompts (num_proc=24): 67%|███████████████████████▎ | 1690/2535 [00:06<00:01, 582.65 examples/s] Tokenizing Prompts (num_proc=24): 71%|████████████████████████▊ | 1795/2535 [00:07<00:01, 513.55 examples/s] Tokenizing Prompts (num_proc=24): 75%|██████████████████████████▏ | 1900/2535 [00:07<00:01, 356.77 examples/s] Tokenizing Prompts (num_proc=24): 79%|███████████████████████████▋ | 2006/2535 [00:08<00:01, 270.39 examples/s] Tokenizing Prompts (num_proc=24): 83%|█████████████████████████████▏ | 2112/2535 [00:09<00:02, 202.48 examples/s] Tokenizing Prompts (num_proc=24): 87%|██████████████████████████████▌ | 2218/2535 [00:09<00:01, 222.42 examples/s] Tokenizing Prompts (num_proc=24): 92%|████████████████████████████████ | 2323/2535 [00:10<00:00, 213.17 examples/s] Tokenizing Prompts (num_proc=24): 96%|█████████████████████████████████▌ | 2429/2535 [00:10<00:00, 239.41 examples/s] Tokenizing Prompts (num_proc=24): 100%|████████████████████████████████████| 2535/2535 [00:13<00:00, 89.06 examples/s] Tokenizing Prompts (num_proc=24): 100%|███████████████████████████████████| 2535/2535 [00:13<00:00, 186.31 examples/s]
[2025-11-26 03:25:52,171] [WARNING] [huggingface_hub.repocard.content:108] [PID:64100] Repo card metadata block was not found. Setting CardData to empty.
[2025-11-26 03:25:53,391] [INFO] [axolotl.utils.data.wrappers.get_dataset_wrapper:87] [PID:64100] Loading dataset: ToastyPigeon/tulu-mini with base_type: chat_template and prompt_style: None
Tokenizing Prompts (num_proc=24): 0%| | 0/43790 [00:00<?, ? examples/s] Tokenizing Prompts (num_proc=24): 2%|▊ | 1000/43790 [00:02<01:56, 366.05 examples/s] Tokenizing Prompts (num_proc=24): 5%|█▌ | 2000/43790 [00:03<01:08, 609.96 examples/s] Tokenizing Prompts (num_proc=24): 7%|██▎ | 3000/43790 [00:03<00:43, 938.14 examples/s] Tokenizing Prompts (num_proc=24): 9%|███ | 4000/43790 [00:04<00:28, 1381.41 examples/s] Tokenizing Prompts (num_proc=24): 18%|██████ | 8000/43790 [00:04<00:08, 3979.88 examples/s] Tokenizing Prompts (num_proc=24): 23%|███████▎ | 10000/43790 [00:04<00:09, 3732.18 examples/s] Tokenizing Prompts (num_proc=24): 25%|████████ | 11000/43790 [00:05<00:08, 3783.00 examples/s] Tokenizing Prompts (num_proc=24): 29%|█████████▎ | 12825/43790 [00:05<00:06, 4896.77 examples/s] Tokenizing Prompts (num_proc=24): 33%|██████████▌ | 14475/43790 [00:05<00:05, 5426.74 examples/s] Tokenizing Prompts (num_proc=24): 39%|████████████▌ | 17125/43790 [00:05<00:03, 8024.04 examples/s] Tokenizing Prompts (num_proc=24): 43%|█████████████▊ | 18950/43790 [00:05<00:03, 7965.45 examples/s] Tokenizing Prompts (num_proc=24): 47%|███████████████▏ | 20775/43790 [00:06<00:02, 8363.22 examples/s] Tokenizing Prompts (num_proc=24): 59%|██████████████████▏ | 25775/43790 [00:06<00:01, 14875.75 examples/s] Tokenizing Prompts (num_proc=24): 65%|████████████████████▏ | 28600/43790 [00:06<00:01, 10170.57 examples/s] Tokenizing Prompts (num_proc=24): 71%|██████████████████████▊ | 31249/43790 [00:07<00:01, 6509.34 examples/s] Tokenizing Prompts (num_proc=24): 75%|████████████████████████ | 32898/43790 [00:07<00:01, 7073.35 examples/s] Tokenizing Prompts (num_proc=24): 79%|█████████████████████████▏ | 34546/43790 [00:07<00:01, 7570.30 examples/s] Tokenizing Prompts (num_proc=24): 83%|██████████████████████████▍ | 36194/43790 [00:07<00:00, 8196.35 examples/s] Tokenizing Prompts (num_proc=24): 86%|███████████████████████████▋ | 37842/43790 [00:08<00:00, 9217.97 examples/s] Tokenizing Prompts (num_proc=24): 90%|████████████████████████████▊ | 39492/43790 [00:08<00:00, 6227.85 examples/s] Tokenizing Prompts (num_proc=24): 94%|██████████████████████████████ | 41141/43790 [00:08<00:00, 5357.82 examples/s] Tokenizing Prompts (num_proc=24): 96%|██████████████████████████████▋ | 41966/43790 [00:09<00:00, 4723.69 examples/s] Tokenizing Prompts (num_proc=24): 98%|███████████████████████████████▍| 42966/43790 [00:10<00:00, 2314.47 examples/s] Tokenizing Prompts (num_proc=24): 100%|████████████████████████████████| 43790/43790 [00:11<00:00, 1655.33 examples/s] Tokenizing Prompts (num_proc=24): 100%|████████████████████████████████| 43790/43790 [00:11<00:00, 3719.04 examples/s]
Dropping Long Sequences (>4096) (num_proc=24): 0%| | 0/132318 [00:00<?, ? examples/s] Dropping Long Sequences (>4096) (num_proc=24): 1%|▏ | 1000/132318 [00:01<02:22, 922.06 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 8%|█▎ | 10000/132318 [00:01<00:11, 11073.18 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 16%|██▋ | 21000/132318 [00:01<00:04, 24182.19 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 21%|███▌ | 28000/132318 [00:01<00:04, 23802.12 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 30%|█████▏ | 40000/132318 [00:01<00:02, 37748.88 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 36%|██████▏ | 48000/132318 [00:01<00:02, 41263.96 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 42%|███████ | 55000/132318 [00:02<00:02, 34709.41 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 48%|████████ | 63000/132318 [00:02<00:01, 41579.13 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 53%|████████▉ | 70000/132318 [00:02<00:01, 46552.60 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 58%|█████████▉ | 77000/132318 [00:02<00:01, 37040.55 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 66%|███████████▏ | 87000/132318 [00:02<00:00, 47883.31 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 71%|████████████ | 94000/132318 [00:02<00:00, 48432.39 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 76%|████████████ | 100000/132318 [00:03<00:00, 38109.09 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 82%|█████████████▏ | 109000/132318 [00:03<00:00, 47152.44 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 88%|██████████████ | 116514/132318 [00:03<00:00, 52490.56 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 94%|███████████████ | 124105/132318 [00:03<00:00, 57668.18 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 99%|███████████████▉| 131292/132318 [00:03<00:00, 52604.65 examples/s] Dropping Long Sequences (>4096) (num_proc=24): 100%|████████████████| 132318/132318 [00:03<00:00, 33596.79 examples/s]
Drop Samples with Zero Trainable Tokens (num_proc=24): 0%| | 0/123829 [00:00<?, ? examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 1%| | 1000/123829 [00:01<02:28, 826.71 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 11%|█ | 14000/123829 [00:01<00:07, 14416.15 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 19%|█▋ | 23000/123829 [00:01<00:04, 23616.20 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 25%|██▎ | 31000/123829 [00:01<00:03, 25687.85 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 34%|███ | 42000/123829 [00:01<00:02, 37598.53 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 40%|███▋ | 50000/123829 [00:02<00:01, 37701.53 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 47%|████▏ | 58000/123829 [00:02<00:01, 44307.59 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 56%|█████ | 69000/123829 [00:02<00:00, 56119.64 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 62%|█████▌ | 77000/123829 [00:02<00:00, 50779.32 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 69%|██████▏ | 85000/123829 [00:02<00:00, 55240.90 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 75%|██████▊ | 93000/123829 [00:02<00:00, 59724.58 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 81%|██████▍ | 100160/123829 [00:02<00:00, 56181.91 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 89%|███████▏| 110437/123829 [00:02<00:00, 66364.79 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 97%|███████▋| 119871/123829 [00:03<00:00, 72983.46 examples/s] Drop Samples with Zero Trainable Tokens (num_proc=24): 100%|████████| 123829/123829 [00:03<00:00, 34971.15 examples/s]
Add position_id column (Sample Packing) (num_proc=24): 0%| | 0/123829 [00:00<?, ? examples/s] Add position_id column (Sample Packing) (num_proc=24): 1%| | 1000/123829 [00:01<02:34, 797.52 examples/s] Add position_id column (Sample Packing) (num_proc=24): 16%|█▍ | 20000/123829 [00:01<00:05, 20005.84 examples/s] Add position_id column (Sample Packing) (num_proc=24): 25%|██▎ | 31000/123829 [00:01<00:04, 19157.66 examples/s] Add position_id column (Sample Packing) (num_proc=24): 36%|███▎ | 45000/123829 [00:02<00:02, 31006.41 examples/s] Add position_id column (Sample Packing) (num_proc=24): 44%|███▉ | 55000/123829 [00:02<00:02, 24605.20 examples/s] Add position_id column (Sample Packing) (num_proc=24): 54%|████▊ | 67000/123829 [00:02<00:01, 33902.10 examples/s] Add position_id column (Sample Packing) (num_proc=24): 61%|█████▌ | 76000/123829 [00:03<00:02, 18577.96 examples/s] Add position_id column (Sample Packing) (num_proc=24): 75%|██████▊ | 93000/123829 [00:03<00:01, 29649.99 examples/s] Add position_id column (Sample Packing) (num_proc=24): 82%|██████▌ | 102000/123829 [00:04<00:00, 23975.20 examples/s] Add position_id column (Sample Packing) (num_proc=24): 95%|███████▌| 117958/123829 [00:04<00:00, 34831.96 examples/s] Add position_id column (Sample Packing) (num_proc=24): 100%|████████| 123829/123829 [00:06<00:00, 20598.27 examples/s]
Saving the dataset (0/24 shards): 0%| | 0/123829 [00:00<?, ? examples/s] Saving the dataset (0/24 shards): 2%|▌ | 2000/123829 [00:00<00:37, 3248.08 examples/s] Saving the dataset (1/24 shards): 49%|███████████████ | 60320/123829 [00:00<00:19, 3248.08 examples/s] Saving the dataset (2/24 shards): 53%|████████████████▎ | 65320/123829 [00:00<00:18, 3248.08 examples/s] Saving the dataset (3/24 shards): 63%|███████████████████▍ | 77480/123829 [00:00<00:14, 3248.08 examples/s] Saving the dataset (4/24 shards): 66%|████████████████████▍ | 81800/123829 [00:00<00:12, 3248.08 examples/s] Saving the dataset (5/24 shards): 67%|████████████████████▊ | 82960/123829 [00:00<00:12, 3248.08 examples/s] Saving the dataset (6/24 shards): 67%|████████████████████▊ | 83120/123829 [00:00<00:12, 3248.08 examples/s] Saving the dataset (7/24 shards): 67%|████████████████████▊ | 83120/123829 [00:00<00:12, 3248.08 examples/s] Saving the dataset (8/24 shards): 70%|█████████████████████▌ | 86280/123829 [00:00<00:11, 3248.08 examples/s] Saving the dataset (9/24 shards): 74%|██████████████████████▉ | 91440/123829 [00:00<00:09, 3248.08 examples/s] Saving the dataset (10/24 shards): 74%|██████████████████████▏ | 91600/123829 [00:00<00:09, 3248.08 examples/s] Saving the dataset (11/24 shards): 76%|██████████████████████▋ | 93760/123829 [00:00<00:09, 3248.08 examples/s] Saving the dataset (12/24 shards): 77%|███████████████████████▏ | 95919/123829 [00:00<00:08, 3248.08 examples/s] Saving the dataset (13/24 shards): 78%|███████████████████████▌ | 97079/123829 [00:00<00:08, 3248.08 examples/s] Saving the dataset (13/24 shards): 80%|██████████████████████▍ | 99079/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (14/24 shards): 84%|██████████████████████▋ | 104238/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (15/24 shards): 86%|███████████████████████▏ | 106397/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (16/24 shards): 87%|███████████████████████▍ | 107557/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (17/24 shards): 89%|███████████████████████▉ | 109716/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (18/24 shards): 90%|████████████████████████▏ | 110875/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (19/24 shards): 95%|█████████████████████████▌ | 117193/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (20/24 shards): 95%|█████████████████████████▌ | 117193/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (21/24 shards): 97%|██████████████████████████▏| 120352/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (22/24 shards): 98%|██████████████████████████▍| 121511/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (23/24 shards): 99%|██████████████████████████▋| 122670/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (24/24 shards): 100%|███████████████████████████| 123829/123829 [00:00<00:00, 185422.31 examples/s] Saving the dataset (24/24 shards): 100%|███████████████████████████| 123829/123829 [00:00<00:00, 149566.99 examples/s]
Using Liger RMSNorm!
Loading checkpoint shards: 0%| | 0/6 [00:00<?, ?it/s] Loading checkpoint shards: 100%|███████████████████████████████████████████████████████| 6/6 [00:00<00:00, 519.79it/s]
Loading checkpoint shards: 0%| | 0/6 [00:00<?, ?it/s] Loading checkpoint shards: 100%|███████████████████████████████████████████████████████| 6/6 [00:00<00:00, 544.77it/s]
[2025-11-26 14:54:12,836] [WARNING] [py.warnings._showwarnmsg:110] [PID:64100] /root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:680: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
warnings.warn(