Add model card metadata and links

Hi! I'm Niels from the Hugging Face community science team.

I noticed this repository could benefit from more detailed metadata and links to help researchers find and use the model. This PR:
- Adds YAML metadata (`pipeline_tag`, `library_name`, and `license`).
- Links the model repository to the research paper and the official GitHub repository.
- Provides a summary of the model's purpose based on the paper abstract.

Let me know if you have any questions!

README.md

@@ -1,20 +1,33 @@
-
+---
 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.
+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).
+
+- **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
+
+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. 
+
+AtomThinkPRM is designed for process supervision, enabling the evaluation of single-step reasoning quality within these multimodal chains.
 
 # Citation
-If you use this dataset in your research, please cite:
-```text
+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},