graphg 0.1.0.post20250930

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

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📝 What is GraphGen?

GraphGen is a framework for synthetic data generation guided by knowledge graphs. Please check the paper and best practice.

Here is post-training result which over 50% SFT data comes from GraphGen and our data clean pipeline.

It begins by constructing a fine-grained knowledge graph from the source text，then identifies knowledge gaps in LLMs using the expected calibration error metric, prioritizing the generation of QA pairs that target high-value, long-tail knowledge. Furthermore, GraphGen incorporates multi-hop neighborhood sampling to capture complex relational information and employs style-controlled generation to diversify the resulting QA data.

After data generation, you can use LLaMA-Factory and xtuner to finetune your LLMs.

📌 Latest Updates

• 2025.09.29: We auto-update gradio demo on Hugging Face and ModelScope.
• 2025.08.14: We have added support for community detection in knowledge graphs using the Leiden algorithm, enabling the synthesis of Chain-of-Thought (CoT) data.
• 2025.07.31: We have added Google, Bing, Wikipedia, and UniProt as search back-ends.
• 2025.04.21: We have released the initial version of GraphGen.

🚀 Quick Start

Experience GraphGen through Web or Backup Web Entrance

For any questions, please check FAQ, open new issue or join our wechat group and ask.

Preparation

1. Install uv

   # You could try pipx or pip to install uv when meet network issues, refer the uv doc for more details
   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Clone the repository

   git clone --depth=1 https://github.com/open-sciencelab/GraphGen
   cd GraphGen
   

3. Create a new uv environment

    uv venv --python 3.10
   

4. Configure the dependencies

   uv pip install -r requirements.txt
   

Run Gradio Demo

python -m webui.app

Run from PyPI

1. Install GraphGen

   uv pip install graphg
   

2. Run in CLI

   SYNTHESIZER_MODEL=your_synthesizer_model_name \
   SYNTHESIZER_BASE_URL=your_base_url_for_synthesizer_model \
   SYNTHESIZER_API_KEY=your_api_key_for_synthesizer_model \
   TRAINEE_MODEL=your_trainee_model_name \
   TRAINEE_BASE_URL=your_base_url_for_trainee_model \
   TRAINEE_API_KEY=your_api_key_for_trainee_model \
   graphg --output_dir cache
   

Run from Source

1. Configure the environment

   • Create an .env file in the root directory

     cp .env.example .env
     

   • Set the following environment variables:

     # Synthesizer is the model used to construct KG and generate data
     SYNTHESIZER_MODEL=your_synthesizer_model_name
     SYNTHESIZER_BASE_URL=your_base_url_for_synthesizer_model
     SYNTHESIZER_API_KEY=your_api_key_for_synthesizer_model
     # Trainee is the model used to train with the generated data
     TRAINEE_MODEL=your_trainee_model_name
     TRAINEE_BASE_URL=your_base_url_for_trainee_model
     TRAINEE_API_KEY=your_api_key_for_trainee_model
     

2. (Optional) Customize generation parameters in graphgen/configs/ folder.

   Edit the corresponding YAML file, e.g.:

     # configs/cot_config.yaml
     input_file: resources/input_examples/jsonl_demo.jsonl
     output_data_type: cot
     tokenizer: cl100k_base
     # additional settings...
   

3. Generate data

   Pick the desired format and run the matching script:

   Format	Script to run	Notes
   cot	bash scripts/generate/generate_cot.sh	Chain-of-Thought Q&A pairs
   atomic	bash scripts/generate/generate_atomic.sh	Atomic Q&A pairs covering basic knowledge
   aggregated	bash scripts/generate/generate_aggregated.sh	Aggregated Q&A pairs incorporating complex, integrated knowledge
   multi-hop	bash scripts/generate/generate_multihop.sh	Multi-hop reasoning Q&A pairs

4. Get the generated data

   ls cache/data/graphgen
   

Run with Docker

1. Build the Docker image

   docker build -t graphgen .
   

2. Run the Docker container

    docker run -p 7860:7860 graphgen
   

🏗️ System Architecture

See analysis by deepwiki for a technical overview of the GraphGen system, its architecture, and core functionalities.

Workflow

🍀 Acknowledgements

• SiliconFlow Abundant LLM API, some models are free
• LightRAG Simple and efficient graph retrieval solution
• ROGRAG A robustly optimized GraphRAG framework
• DB-GPT An AI native data app development framework

📚 Citation

If you find this repository useful, please consider citing our work:

@misc{chen2025graphgenenhancingsupervisedfinetuning,
      title={GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation}, 
      author={Zihong Chen and Wanli Jiang and Jinzhe Li and Zhonghang Yuan and Huanjun Kong and Wanli Ouyang and Nanqing Dong},
      year={2025},
      eprint={2505.20416},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20416}, 
}

📜 License

This project is licensed under the Apache License 2.0.

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