Fine-Tuning Llama 3 for RAG Q&A: A Step-by-Step Guide

In this video, Venelin Valkov demonstrates how to fine-tune the Llama 3 8B Instruct model on a custom dataset for a Retrieval-Augmented Generation (RAG) Q&A use case, specifically focusing on financial data. The process involves several steps, including preparing a custom dataset, evaluating the base model, setting up a LoRA (Low-Rank Adaptation) adapter for efficient training on a single GPU, and finally training and evaluating the fine-tuned model. Valkov uses a Google Colab notebook to illustrate the process, starting with installing necessary libraries, loading the model and tokenizer, and transforming a dataset from a JSON file into a suitable format for training. He emphasizes the importance of adding a padding token to the tokenizer to avoid issues during training. The dataset is split into training, validation, and test sets, and the model’s initial performance is evaluated. The training process involves using the TRL supervised fine-tuning trainer and the LoRA adapter to fine-tune the model efficiently. After training, the fine-tuned model is compared to the base model, showing significant improvements in generating concise and relevant responses. The video concludes with an evaluation of the model’s performance and a discussion on the benefits of fine-tuning for specific tasks.