How to Fine-Tune LLaMA-2

Introduction

In this tutorial, we'll explore Metas LLaMA-2, a large language model (LLM), and demonstrate how to fine-tune it on a new dataset using Google Colab. Additionally, we'll cover new methodologies and fine-tuning techniques.

Fine-Tuning LLaMA-2

LLaMA-2 is Metas second-generation open-source LLM collection and uses an optimized transformer architecture offering improved language understanding and generation capabilities. Fine-tuning LLaMA-2 allows you to adapt the model to a specific task or domain, enhancing its performance for tasks such as sentiment analysis, question answering, and text summarization.