GraphWalker-7B

📄 Paper (arXiv:2603.28533) | 💻 GitHub | 🤗 Model

GraphWalker-7B is a specialized large language model fine-tuned from Qwen2.5-7B-Instruct for Agentic Knowledge Graph Question Answering (KGQA). GraphWalker learns to navigate knowledge graphs via synthetic trajectory curriculum — achieving strong generalization with a single, compact 7B model.

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🌟 Overview

GraphWalker is an agentic framework for multi-turn Knowledge Graph Question Answering (KGQA) over Global Knowledge Graphs (e.g., Freebase). It transforms LLMs into reasoning agents that autonomously navigate massive KGs through a "Think-Query-Observe" loop, optimized via a synthetic curriculum.

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🛠️ Usage

1. Environment Setup

pip install vllm transformers

2. Download the Model

# Via huggingface-cli
huggingface-cli download xushuwen23/GraphWalker-7B --local-dir ./GraphWalker-7B

3. Inference with vLLM (Recommended)

Start the vLLM server:

vllm serve "xushuwen23/GraphWalker-7B" \
    --host 0.0.0.0 --port 22240 \
    --served-model-name graphwalker-7b \
    --gpu-memory-utilization 0.9 \
    --dtype auto \
    --chat-template "./GraphWalker-7B/chat_template.jinja"

For training and evaluation, see 💻 GitHub for details.

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📈 Evaluation Results

Method	Backbone	CWQ EM	CWQ F1	WebQSP EM	WebQSP F1
GraphWalker
†Vanilla Agent	Qwen2.5-7B-Instruct	40.7	33.2	68.4	66.1
†Vanilla Agent	GPT-4o-mini	63.4	60.3	79.6	70.6
†Vanilla Agent	DeepSeek-V3.2	69.8	63.5	76.7	71.8
GraphWalker-7B-SFT	Qwen2.5-7B-Instruct	68.3	63.2	82.0	79.1
GraphWalker-3B-SFT-RL	Qwen2.5-3B-Instruct	70.9	65.2	83.5	81.7
GraphWalker-8B-SFT-RL	LLaMA3.1-8B-Instruct	78.5	69.6	88.2	84.5
GraphWalker-7B-SFT-RL	Qwen2.5-7B-Instruct	79.6	74.2	91.5	88.6

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📝 Citation

If you use GraphWalker-7B or find this work helpful, please cite:

@misc{xu2026graphwalkeragenticknowledgegraph,
      title={GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum}, 
      author={Shuwen Xu and Yao Xu and Jiaxiang Liu and Chenhao Yuan and Wenshuo Peng and Jun Zhao and Kang Liu},
      year={2026},
      eprint={2603.28533},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.28533}, 
}

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📄 License

This model is released under the Apache 2.0 License, consistent with the base model Qwen2.5-7B-Instruct.