Fine-tuning Llama 2 for news category prediction: A step-by-step comprehensive guide to fine-tuning any LLM (Part 1)

In this blog, I will guide you through the process of fine-tuning Meta’s Llama 2 7B model for news article categorization across 18 different categories. I will utilize a news classification instruction dataset that I previously created using GPT 3.5. If you’re interested in how I generated that dataset and the motivation behind this mini-project, you can refer to my earlier <a href="https://medium.com/@kshitiz.sahay26/how-i-created-an-instruction-dataset-using-gpt-3-5-to-fine-tune-llama-2-for-news-classification-ed02fe41c81f" rel="noopener">blog</a> or <a href="https://colab.research.google.com/drive/16rZ8DlvQp5YJED1ECUNbLKbu2YWLcLST?usp=sharing" rel="noopener ugc nofollow" target="_blank">notebook</a> where I discuss the details. The purpose of this notebook is to provide a comprehensive, step-by-step tutorial for fine-tuning any LLM (Large Language Model). Unlike many tutorials available, I’ll explain each step in a detailed manner, covering all classes, functions, and parameters used. This guide will be divided into two parts: Part 1: Setting up and Preparing for Fine-Tuning [This blog] <ol> <li>Installing and loading the required modules</li> <li>Steps to get approval for Meta’s Llama 2 family of models</li> <li>Setting up Hugging Face CLI and user authentication</li> <li>Loading a pre-trained model and its associated tokenizer</li> <li>Loading the training dataset</li> <li>Preprocessing the training dataset for model fine-tuning</li> </ol> <a href="https://medium.com/@kshitiz.sahay26/fine-tuning-llama-2-for-news-category-prediction-a-step-by-step-comprehensive-guide-to-deeccf3e3a88">Click Here</a>