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---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: step-conditional-control
tags:
- generated_from_trainer
- trl
- sft
license: apache-2.0
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Model Summary
- **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
- **Paper:** https://arxiv.org/abs/2501.19393
# Use
This is the token-conditional control model for our paper. You can evaluate using the information [here](https://github.com/simplescaling/s1?tab=readme-ov-file#evaluation).
# Training information
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hashimoto-group/o1/runs/xaantfal)
- TRL: 0.13.0
- Transformers: 4.48.0
- Pytorch: 2.3.1
- Datasets: 3.0.1
- Tokenizers: 0.21.0
# Citation
```bibtex
@misc{muennighoff2025s1simpletesttimescaling,
title={s1: Simple test-time scaling},
author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto},
year={2025},
eprint={2501.19393},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.19393},
}
``` |