Update README.md

README.md

@@ -1,3 +1,50 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- hotpotqa/hotpot_qa
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+---
+
+
+## Model Card for RAG-R1
+
+### Model Details
+
+* **Model Name:** RAG-R1-sq-7b
+* **Version:** 1.0
+* **Model Type:** RAG
+* **Developers:** Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu
+
+[![Paper](https://img.shields.io/badge/arXiv-2507.02962-b5212f.svg)](https://arxiv.org/abs/2507.02962) [![Code](https://img.shields.io/badge/GitHub-Repository-blue.svg?logo=github)](https://github.com/inclusionAI/AWorld-RL/tree/main/RAG-R1)
+
+### Overview
+
+RAG-R1 is a deepsearch training framework designed to enable LLMs to adaptively leverage internal and external knowledge during the reasoning process. 
+We further expand the generation and retrieval processes within the framework from single-query mode to multi-query parallelism, aimed at reducing inference time and enhancing the model's capabilities. 
+Extensive experiments on seven question-answering benchmarks demonstrate that our method outperforms the strongest baseline by up to 13.2% and decreases inference time by 11.1%.
+
+### Framework
+
+<img src="RAG-R1.png" style="width:100%;">
+<h5 align="center"> Overall framework of RAG-R1.</h5>
+
+### Performance
+
+<img src="RAG-R1-result.png" style="width:100%;">
+<h5 align="left">Performance comparisons on QA benchmarks under the EM metric. The best and second
+best results are bold and underlined, respectively.</h5>
+
+### Acknowledgements
+RAG-R1 is inspired by [Deepseek-R1](https://github.com/deepseek-ai/DeepSeek-R1) with its implementation based on [veRL](https://github.com/volcengine/verl) and [Search-r1](https://github.com/PeterGriffinJin/Search-R1). We deeply appreciate the contributions of these teams to open-source research and development.
+
+### Citation
+Please cite our repo if our works are helpful for your research.
+```
+@article{RAG-R1,
+  title={RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism}, 
+  author={Zhiwen Tan and Jiaming Huang and Qintong Wu and Hongxuan Zhang and Chenyi Zhuang and Jinjie Gu},
+  journal={arXiv preprint arXiv:2507.02962},
+  year={2025}
+}
+```