This project provides a comprehensive WebUI platform for integrating and utilizing various large language models (LLMs), targeting developers and power users who want to build custom LLM applications without extensive coding. It offers a full-process toolchain for model deployment, fine-tuning, and application development, including features like knowledge base integration, role-playing, and image generation.

How It Works

LLM-Kit is designed as an integrated package, simplifying the complex setup and interaction with diverse LLMs. It supports both API-based access to commercial models (like OpenAI, ERNIE Bot) and local inference/training of open-source models. The platform leverages Gradio for its WebUI, providing a user-friendly interface for managing models, data, and application features. Key architectural choices include modular design for different functionalities (chat, image generation, TTS, etc.) and support for various fine-tuning techniques like LoRA and full parameter tuning.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.8-3.10, CUDA 11.7/11.8 (for GPU acceleration), Windows or Linux OS. GPU drivers and CUDA toolkit must be installed separately.
• Run: Execute web-demo-CN.bat (Windows) or web-demo-CN.sh (Linux).
• Docs: 说明文档 (Note: Link points to the main README, not a separate doc page).

Highlighted Details

• Supports a wide array of LLM APIs and open-source models, including quantized versions.
• Offers extensive fine-tuning capabilities (LoRA, P-Tuning, full parameter).
• Integrates multi-modal features like Midjourney image generation and Live2D character rendering.
• Includes tools for dataset creation, knowledge base management (FAISS, MySQL), and agent development.

Maintenance & Community

The project is initiated by Wu Pingyu and has contributors from institutions like CAS Institute of Computing Technology and UCL. The README indicates active development with a roadmap and calls for contributors. Community links (Discord/Slack) are not explicitly provided in the README.

Licensing & Compatibility

Licensed under AGPL-3.0. This license requires derivative works to be open-sourced if distributed. Commercial use or closed-source linking of modified versions is restricted unless a commercial license is obtained from the project maintainers (wupingyu@jc-data.cn).

Limitations & Caveats

Some advanced features like DeepSpeed training and bitsandbytes quantization are Linux-specific. Inference with DeepSpeed on Windows requires compilation. Midjourney integration requires a VPN. The project is actively developed, implying potential for breaking changes or incomplete features.