#finetune

This framework facilitates AI model workflows by providing streamlined pretraining, finetuning, and deployment for flexible and scalable use, especially among PyTorch users. It integrates smoothly with LitServe for model serving, ensuring efficient workflow management. Designed with practicality in mind, it effortlessly handles classification, segmentation, and summarization tasks. Supported by comprehensive documentation and community-driven examples, it serves as a vital resource for various deep learning projects.

This project details a complete process for tuning the ChatGLM large language model through LoRA and Reinforcement Learning with Human Feedback (RLHF) on accessible hardware. It covers data processing, supervised fine-tuning, and reward modeling. The guide also addresses effective PEFT version utilization for model integration, overcoming Hugging Face transformer compatibility challenges. This enables efficient model development and tuning, specifically for those working with constrained resources.

The project delivers a complete pathway for tuning the Vicuna Language Model with LoRA and RLHF methodologies on consumer hardware such as the 2080Ti GPU. It includes comprehensive steps for acquiring Vicuna weights, executing supervised fine-tuning, and incorporating PEFT and reward model adapters. Key phases involve managing CUDA memory and version compatibility challenges, enabling effective model training management. References to FastChat and alpaca-lora provide robust setup support for facilitating advanced machine learning tasks in constrained resource environments.