Fine-Tuning LLaMA 2: A Beginner’s Guide with Practical Steps

Introduction

Large Language Models (LLMs) like Meta’s LLaMA 2 have revolutionized natural language processing. But out of the box, they’re trained on general internet data and may not perform well for your specific domain or task.

That’s where fine-tuning comes in.

In this article, I’ll walk you through:

• The concept of fine-tuning LLMs

• Key techniques like LoRA and PEFT

• A practical guide to fine-tuning LLaMA 2 using Hugging Face and Colab

• Sample code to get you started

Let’s dive in.

What Is Fine-Tuning?

Fine-tuning is the process of taking a pre-trained model (like LLaMA 2) and continuing training it on a task-specific dataset to improve its performance for that task.

For example, you might fine-tune LLaMA 2 on:

• Legal documents → to build a legal advisor bot

• Medical conversations → for clinical assistant tasks

• Customer support logs → for chatbot automation

What Is LLaMA 2?

LLaMA 2 is a family of open-weight LLMs developed by Meta AI, released in July 2023. Key highlights:

• Available in 7B, 13B, and 65B parameter sizes

• Trained on 2 trillion tokens

• Released for research and commercial use (with license approval)

• Hosted on Hugging Face

Key Fine-Tuning Concepts

Before jumping into code, here are some key terms:

Full Fine-Tuning

• All model weights are updated during training.

• Very resource-intensive (requires multiple GPUs).

Parameter-Efficient Fine-Tuning (PEFT)

• Only a small subset of model parameters are trained.

• Uses adapters like LoRA (Low-Rank Adaptation).

• Much faster and cheaper—ideal for Colab or single GPU setups.

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Setup: Tools & Libraries

We’ll be using:

Step-by-Step Fine-Tuning of LLaMA 2

I performed this on Google Colab (T4 GPU).

Step 1: Installing all the required packages

Step 2 : Import all required packages

Step 3