96 lines
3.1 KiB
Markdown
96 lines
3.1 KiB
Markdown
---
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- LLM Agent
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- Knowledge Graph
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- Question Answering
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- Reasoning
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---
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# GraphWalker-7B
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[**📄 Paper (arXiv:2603.28533)**](https://arxiv.org/abs/2603.28533) | [**💻 GitHub**](https://github.com/XuShuwenn/GraphWalker) | [**🤗 Model**](https://huggingface.co/xushuwen23/GraphWalker-7B)
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**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.
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---
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## 🌟 Overview
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**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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---
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## 🛠️ Usage
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### 1. Environment Setup
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```bash
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pip install vllm transformers
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```
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### 2. Download the Model
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```bash
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# Via huggingface-cli
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huggingface-cli download xushuwen23/GraphWalker-7B --local-dir ./GraphWalker-7B
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```
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### 3. Inference with vLLM (Recommended)
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**Start the vLLM server:**
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```bash
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vllm serve "xushuwen23/GraphWalker-7B" \
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--host 0.0.0.0 --port 22240 \
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--served-model-name graphwalker-7b \
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--gpu-memory-utilization 0.9 \
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--dtype auto \
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--chat-template "./GraphWalker-7B/chat_template.jinja"
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```
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For training and evaluation, see [**💻 GitHub**](https://github.com/XuShuwenn/GraphWalker) for details.
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---
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## 📈 Evaluation Results
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| Method | Backbone | CWQ EM | CWQ F1 | WebQSP EM | WebQSP F1 |
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|:---|:---|:---:|:---:|:---:|:---:|
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| **GraphWalker** | | | | | |
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| †Vanilla Agent | Qwen2.5-7B-Instruct | 40.7 | 33.2 | 68.4 | 66.1 |
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| †Vanilla Agent | GPT-4o-mini | 63.4 | 60.3 | 79.6 | 70.6 |
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| †Vanilla Agent | DeepSeek-V3.2 | 69.8 | 63.5 | 76.7 | 71.8 |
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| GraphWalker-7B-SFT | Qwen2.5-7B-Instruct | 68.3 | 63.2 | 82.0 | 79.1 |
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| GraphWalker-3B-SFT-RL | Qwen2.5-3B-Instruct | 70.9 | 65.2 | 83.5 | 81.7 |
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| GraphWalker-8B-SFT-RL | LLaMA3.1-8B-Instruct | <u>78.5</u> | 69.6 | <u>88.2</u> | <u>84.5</u> |
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| **GraphWalker-7B-SFT-RL** | **Qwen2.5-7B-Instruct** | **79.6** | **74.2** | **91.5** | **88.6** |
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---
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## 📝 Citation
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If you use GraphWalker-7B or find this work helpful, please cite:
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```bibtex
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@misc{xu2026graphwalkeragenticknowledgegraph,
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title={GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum},
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author={Shuwen Xu and Yao Xu and Jiaxiang Liu and Chenhao Yuan and Wenshuo Peng and Jun Zhao and Kang Liu},
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year={2026},
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eprint={2603.28533},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2603.28533},
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}
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```
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---
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## 📄 License
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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). |