| --- |
| license: apache-2.0 |
| library_name: transformers |
| pipeline_tag: image-text-to-text |
| --- |
| |
| # Model Card for AtomThinkPRM |
|
|
| The model is fine-tuned with atomic step execution based on math-psa and can be used for process supervision in multimodal reasoning chains. It is part of the **AtomThink** framework, which introduces "slow thinking" into multimodal large language models (MLLMs). |
|
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| - **Paper:** [AtomThink: Multimodal Slow Thinking with Atomic Step Reasoning](https://huggingface.co/papers/2411.11930) |
| - **Repository:** [https://github.com/Kun-Xiang/AtomThink](https://github.com/Kun-Xiang/AtomThink) |
|
|
| ## Description |
|
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| AtomThink incorporates the notion of "slow thinking" into MLLMs, allowing models to adaptively use different levels of reasoning for questions of varying complexity. It proposes a novel paradigm of Self-structured Chain of Thought (SCoT), which consists of minimal semantic atomic steps. |
|
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| AtomThinkPRM is designed for process supervision, enabling the evaluation of single-step reasoning quality within these multimodal chains. |
|
|
| # Citation |
| If you use this model in your research, please cite: |
|
|
| ```bibtex |
| @article{xiang2024atomthink, |
| title={AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning}, |
| author={Xiang, Kun and Liu, Zhili and Jiang, Zihao and Nie, Yunshuang and Huang, Runhui and Fan, Haoxiang and Li, Hanhui and Huang, Weiran and Zeng, Yihan and Han, Jianhua and others}, |
| journal={arXiv preprint arXiv:2411.11930}, |
| year={2024} |
| } |
| |
| @article{wang2024openr, |
| title={OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models}, |
| author={Wang, Jun and Fang, Meng and Wan, Ziyu and Wen, Muning and Zhu, Jiachen and Liu, Anjie and Gong, Ziqin and Song, Yan and Chen, Lei and Ni, Lionel M and others}, |
| journal={arXiv preprint arXiv:2410.09671}, |
| year={2024} |
| } |
| ``` |
|
|
| # License |
| The checkpoint is released under the Apache 2.0 license. Please ensure proper attribution when using this checkpoint. |
