Instructions to use xushuwen23/GraphWalker-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xushuwen23/GraphWalker-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="xushuwen23/GraphWalker-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("xushuwen23/GraphWalker-7B")
model = AutoModelForMultimodalLM.from_pretrained("xushuwen23/GraphWalker-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xushuwen23/GraphWalker-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xushuwen23/GraphWalker-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xushuwen23/GraphWalker-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xushuwen23/GraphWalker-7B

SGLang

How to use xushuwen23/GraphWalker-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "xushuwen23/GraphWalker-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xushuwen23/GraphWalker-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "xushuwen23/GraphWalker-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xushuwen23/GraphWalker-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xushuwen23/GraphWalker-7B with Docker Model Runner:
```
docker model run hf.co/xushuwen23/GraphWalker-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

🌟 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

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.

📈 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

📝 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}, 
}