Fine-Tuning Llama 3.1 8B with Minimal Data: A Hands-On Guide

Teach Llama 3.1 to generate custom outputs using minimal data, Unsloth, and Google Colab.

Author: Tim Urista

Alpaca Fine Tuning image

Introduction

Fine-tuning large language models (LLMs) like Llama 3.1 8B can seem daunting, especially when considering the computational resources and vast datasets typically required. However, what if I told you that you could fine-tune Llama 3.1 8B in just a few minutes using only a dozen training samples? In this article, we’ll explore an innovative approach to teach Llama 3.1 your desired output format with minimal data and resources.

We’ll leverage the power of Unsloth, a library that optimizes the fine-tuning process, and Google Colab’s free GPU resources. By the end of this guide, you’ll have a customized Llama 3.1 model that generates summaries in a specific JSON format, ready to integrate into your AI projects.

The Objective: Custom Summaries in JSON Format

Imagine you need an AI model that generates summaries of AI-related documents in a structured JSON format. The model should accept an instruction and context and produce a response like this:

{
  "title": "Introduction to Neural Networks",
  "summary_text": "Neural networks are machine learning algorithms inspired by biological neural networks. They process data through layers to recognize patterns. Used in image and speech recognition and more, they require significant data and computational resources.",
  "keywords": [
    "neural networks",
    "machine learning",
    "pattern recognition",
    "biological inspiration",
    "layers",
    "data processing",
    "image recognition",
    "speech recognition"
  ],
  "further_research": "Explore advancements in neural network architectures and their efficiency in various applications."
}

Our goal is to fine-tune Llama 3.1 to produce responses in this format using only 12 training samples.

Why Only 12 Samples?

You might be skeptical about fine-tuning a massive model with such a small dataset. The key lies in leveraging the pre-trained knowledge of Llama 3.1 and focusing on teaching it how to format its responses rather than what…