---
base_model:
- Qwen/Qwen2.5-7B-Instruct
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- LLM Agent
- Knowledge Graph
- Question Answering
- Reasoning
---

# GraphWalker-7B

[**📄 Paper (arXiv:2603.28533)**](https://arxiv.org/abs/2603.28533) | [**💻 GitHub**](https://github.com/XuShuwenn/GraphWalker) | [**🤗 Model**](https://huggingface.co/xushuwen23/GraphWalker-7B)

**GraphWalker-7B** is a specialized large language model fine-tuned from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/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.

---

## 🌟 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.

---


## 🛠️ Usage

### 1. Environment Setup

```bash
pip install vllm transformers
```

### 2. Download the Model

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

### 3. Inference with vLLM (Recommended)

**Start the vLLM server:**

```bash
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**](https://github.com/XuShuwenn/GraphWalker) for details.


---

## 📈 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 | <u>78.5</u> | 69.6 | <u>88.2</u> | <u>84.5</u> |
| **GraphWalker-7B-SFT-RL** | **Qwen2.5-7B-Instruct** | **79.6** | **74.2** | **91.5** | **88.6** |

---

## 📝 Citation

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

```bibtex
@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}, 
}
```

---

## 📄 License

This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base model [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).