fine-tuning

Summary This post is an explanation of this paper:RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. Generative foundation models, such as Large Language Models (LLMs) and diffusion models, have revolutionized AI by achieving human-like content generation. However, they often suffer from Biases – Models can learn and reinforce societal biases present in the training data (e.g., gender, racial, or cultural stereotypes). Ethical Concerns – AI-generated content can be misused for misinformation, deepfakes, or spreading harmful narratives.

Summary Large Language Models (LLMs) offer immense potential, but realizing that potential often requires fine-tuning them on task-specific data. This guide provides a comprehensive overview of LLM fine-tuning, focusing on practical implementation with LLaMA-Factory and the powerful LoRA technique. What is Fine-Tuning? Fine-tuning adapts a pre-trained model to a new, specific task or dataset. It leverages the general knowledge already learned by the model from a massive dataset (source domain) and refines it with a smaller, more specialized dataset (target domain).