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  # ReaRAG-9B
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  <p align="center">
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  🤗 <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>
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  </p>
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- ReaRAG-9B is trained based on 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.
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  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.
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+ ---
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+ datasets:
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+ - THU-KEG/ReaRAG-20k
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+ language:
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+ - en
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+ base_model:
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+ - THUDM/glm-4-9b
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+ pipeline_tag: question-answering
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+ tags:
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+ - rag
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+ - reasoning
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+ ---
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  # ReaRAG-9B
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  <p align="center">
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  🤗 <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>
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  </p>
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+ 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.
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  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.
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