A powerful tool for creating fine-tuning datasets for Large Language Models

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Features • Quick Start • Documentation • Contributing • License

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Overview

Easy Dataset is an application specifically designed for creating fine-tuning datasets for Large Language Models (LLMs). It provides an intuitive interface for uploading domain-specific files, intelligently splitting content, generating questions, and producing high-quality training data for model fine-tuning.

With Easy Dataset, you can transform domain knowledge into structured datasets, compatible with all LLM APIs that follow the OpenAI format, making the fine-tuning process simple and efficient.

Features

• Intelligent Document Processing: Supports intelligent recognition and processing of multiple formats including PDF, Markdown, DOCX, etc.
• Intelligent Text Splitting: Supports multiple intelligent text splitting algorithms and customizable visual segmentation
• Intelligent Question Generation: Extracts relevant questions from each text segment
• Domain Labels: Intelligently builds global domain labels for datasets, with global understanding capabilities
• Answer Generation: Uses LLM API to generate comprehensive answers and Chain of Thought (COT)
• Flexible Editing: Edit questions, answers, and datasets at any stage of the process
• Multiple Export Formats: Export datasets in various formats (Alpaca, ShareGPT) and file types (JSON, JSONL)
• Wide Model Support: Compatible with all LLM APIs that follow the OpenAI format
• User-Friendly Interface: Intuitive UI designed for both technical and non-technical users
• Custom System Prompts: Add custom system prompts to guide model responses

Quick Demo

ed3.mp4

Local Run

Download Client

Windows	MacOS		Linux
Setup.exe	Intel	M	AppImage

Install with NPM

1. Clone the repository:

   git clone https://github.com/ConardLi/easy-dataset.git
   cd easy-dataset

2. Install dependencies:

   npm install

3. Start the development server:

   npm run build

   npm run start

4. Open your browser and visit http://localhost:1717

Using the Official Docker Image

1. Clone the repository:

git clone https://github.com/ConardLi/easy-dataset.git
cd easy-dataset

2. Modify the docker-compose.yml file:

services:
  easy-dataset:
    image: ghcr.io/conardli/easy-dataset
    container_name: easy-dataset
    ports:
      - '1717:1717'
    volumes:
      - ${LOCAL_DB_PATH}:/app/local-db
      - ${LOCAL_PRISMA_PATH}:/app/prisma
    restart: unless-stopped

Note: Replace {YOUR_LOCAL_DB_PATH} and {LOCAL_PRISMA_PATH} with the actual paths where you want to store the local database. It is recommended to use the local-db and prisma folders in the current code repository directory to maintain consistency with the database paths when starting via NPM.

3. Start with docker-compose:

docker-compose up -d

4. Open a browser and visit http://localhost:1717

Building with a Local Dockerfile

If you want to build the image yourself, use the Dockerfile in the project root directory:

1. Clone the repository:

git clone https://github.com/ConardLi/easy-dataset.git
cd easy-dataset

2. Build the Docker image:

docker build -t easy-dataset .

3. Run the container:

docker run -d \
  -p 1717:1717 \
  -v {YOUR_LOCAL_DB_PATH}:/app/local-db \
  -v {LOCAL_PRISMA_PATH}:/app/prisma \
  --name easy-dataset \
  easy-dataset

Note: Replace {YOUR_LOCAL_DB_PATH} and {LOCAL_PRISMA_PATH} with the actual paths where you want to store the local database. It is recommended to use the local-db and prisma folders in the current code repository directory to maintain consistency with the database paths when starting via NPM.

4. Open a browser and visit http://localhost:1717

How to Use

Create a Project

1. Click the "Create Project" button on the homepage;
2. Enter a project name and description;
3. Configure your preferred LLM API settings

Process Documents

1. Upload your files in the "Text Split" section (supports PDF, Markdown, txt, DOCX);
2. View and adjust the automatically split text segments;
3. View and adjust the global domain tree

Generate Questions

2. Batch construct questions based on text blocks;
3. View and edit the generated questions;
4. Organize questions using the label tree

Create Datasets

1. Batch construct datasets based on questions;
2. Generate answers using the configured LLM;
3. View, edit, and optimize the generated answers

Export Datasets

1. Click the "Export" button in the Datasets section;
2. Choose your preferred format (Alpaca or ShareGPT);
3. Select the file format (JSON or JSONL);
4. Add custom system prompts as needed;
5. Export your dataset

Documentation

• View the demo video of this project: Easy Dataset Demo Video
• For detailed documentation on all features and APIs, visit our Documentation Site
• View the paper of this project: Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

Community Practice

• Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge
• Easy Dataset Practical Guide: How to Build High-Quality Datasets?
• Interpretation of Key Feature Updates in Easy Dataset
• Foundation Models Fine-tuning Datasets: Basic Knowledge Popularization

Contributing

We welcome contributions from the community! If you'd like to contribute to Easy Dataset, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Commit your changes (git commit -m 'Add some amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request (submit to the DEV branch)

Please ensure that tests are appropriately updated and adhere to the existing coding style.

Join Discussion Group & Contact the Author

https://docs.easy-dataset.com/geng-duo/lian-xi-wo-men

License

This project is licensed under the AGPL 3.0 License - see the LICENSE file for details.

Citation

If this work is helpful, please kindly cite as:

@misc{miao2025easydataset,
  title={Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents},
  author={Ziyang Miao and Qiyu Sun and Jingyuan Wang and Yuchen Gong and Yaowei Zheng and Shiqi Li and Richong Zhang},
  year={2025},
  eprint={2507.04009},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2507.04009}
}

Star History

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