How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bmbgsj/ProRAG"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bmbgsj/ProRAG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/bmbgsj/ProRAG
Quick Links

Model Card for ProRAG

This model is a fine-tuned version of Qwen/Qwen3-8B based on the methodology described in the paper associated with arXiv ID: 2601.21912.

Model Details

  • Base Model: Qwen3-8B
  • Language: English, Chinese (and others supported by Qwen3)
  • Paper: View on arXiv
  • Library: Transformers

💻 Code & Inference

For inference code, usage examples, and reproduction scripts, please refer to our GitHub repository:

👉 Click here to view the GitHub Repository

(Please verify the details and instructions on the GitHub page.)

Citation

If you use this model or the associated paper in your research, please cite:

@misc{wang2026proragprocesssupervisedreinforcementlearning,
      title={ProRAG: Process-Supervised Reinforcement Learning for Retrieval-Augmented Generation}, 
      author={Zhao Wang and Ziliang Zhao and Zhicheng Dou},
      year={2026},
      eprint={2601.21912},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.21912}, 
}
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