hiyouga/ChatGLM-Efficient-Tuning
An efficient framework for fine-tuning ChatGLM-6B and ChatGLM2-6B models using PEFT methods, including LoRA, QLoRA, and RLHF, with a Web UI.
Core Features
Detailed Introduction
ChatGLM Efficient Tuning provides a robust and accessible framework for customizing large language models, specifically ChatGLM-6B and ChatGLM2-6B. By leveraging Parameter-Efficient Fine-Tuning (PEFT) techniques such as LoRA and QLoRA, it enables users to fine-tune models with reduced computational resources. The project also supports advanced training methodologies like Reinforcement Learning with Human Feedback (RLHF) and offers an intuitive Web UI to streamline the entire model lifecycle from training to deployment. Its OpenAI-compatible API facilitates easy integration into various applications, empowering developers to adapt powerful LLMs for specialized tasks.