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
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…