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qwen-32b-sft-full-arcagi-barc
tttx/qwen-32b-sft-full-arcagi-barc
qwen-32b-sft-full-arcagi-barc is a machine learning model from tttx. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- mit
- Model type
- qwen2
- Access
- Public
- Created
- Feb 3, 2025
- Updated
- Feb 5, 2025
- SHA
- 4c60aa55
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
qwen-32b-sft-full-arcagi-barc
This model is a fine-tuned version of deepseek-ai/Deepseek-R1-Distill-Qwen-32B on the tttx/r1-trajectories-collection-round-2 and the tttx/r1-trajectories-arcagi-barc datasets. It achieves the following results on the evaluation set:
- Loss: 0.4387
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4063 | 1.0 | 328 | 0.4616 |
| 0.3589 | 2.0 | 656 | 0.4407 |
| 0.3658 | 3.0 | 984 | 0.4396 |
| 0.3444 | 4.0 | 1312 | 0.4387 |
Framework versions
- PEFT 0.13.2
- Transformers 4.47.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Library
- peft
- Type
- qwen2
- License
- mit
- Created
- Feb 3, 2025
- Updated
- Feb 5, 2025