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

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GraphGen is a framework for synthetic data generation guided by knowledge graphs. Please check thepaper andbest practice.

Here is post-training result whichover 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 useLLaMA-Factory andxtuner to finetune your LLMs.

• 2025.09.29: We auto-update gradio demo onHugging Face andModelScope.
• 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.

Experience GraphGen throughWeb orBackup Web Entrance

For any questions, please checkFAQ, open newissue or join ourwechat group and ask.

1. Installuv

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

2. Clone the repository

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

3. Create a new uv environment

    uv venv --python 3.10

4. Configure the dependencies

   uv pip install -r requirements.txt

python -m webui.app

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

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 dataSYNTHESIZER_MODEL=your_synthesizer_model_nameSYNTHESIZER_BASE_URL=your_base_url_for_synthesizer_modelSYNTHESIZER_API_KEY=your_api_key_for_synthesizer_model# Trainee is the model used to train with the generated dataTRAINEE_MODEL=your_trainee_model_nameTRAINEE_BASE_URL=your_base_url_for_trainee_modelTRAINEE_API_KEY=your_api_key_for_trainee_model

2. (Optional) Customize generation parameters ingraphgen/configs/ folder.

   Edit the corresponding YAML file, e.g.:

   # configs/cot_config.yamlinput_file:resources/input_examples/jsonl_demo.jsonloutput_data_type:cottokenizer: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

1. Build the Docker image

   docker build -t graphgen.

2. Run the Docker container

    docker run -p 7860:7860 graphgen

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

• 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

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}, }

This project is licensed under theApache License 2.0.