---
datasets:
- THU-KEG/ReaRAG-20k
language:
- en
base_model:
- THUDM/glm-4-9b
pipeline_tag: question-answering
tags:
- rag
- reasoning
---
# ReaRAG-9B
<p align="center">
   🤗 <a href="https://huggingface.co/datasets/THU-KEG/ReaRAG-20k" target="_blank">Dataset</a> • 💻 <a href="https://github.com/THU-KEG/ReaRAG" target="_blank">GitHub</a> • 📃 <a href="https://arxiv.org/abs/2503.21729" target="_blank">Paper</a>
</p>

ReaRAG-9B is trained based on [glm-4-9b](https://huggingface.co/THUDM/glm-4-9b), with enhanced capability to generate knowledge-guided reasoning chains for iterative RAG. The model supports a context window of up to 8k tokens.

Please refer to the [Inference](https://github.com/THU-KEG/ReaRAG?tab=readme-ov-file#%EF%B8%8F-inference) section in the GitHub repository for usage detail.

# 📚 Citation
If you use this dataset in your research or projects, please consider citing our work:
```
@article{lee2025rearag,
  title={ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation},
  author={Lee, Zhicheng and Cao, Shulin and Liu, Jinxin and Zhang, Jiajie and Liu, Weichuan and Che, Xiaoyin and Hou, Lei and Li, Juanzi},
  journal={arXiv preprint arXiv:2503.21729},
  year={2025}
}
```