Boost LLaMA 3.1 Performance by 3% in Just 100 Steps on Google Colab Free Tier | Text Classification

In this video, I’ll guide you through an incredible journey where we fine-tune the state-of-the-art LLaMA 3.1 8B model for a real-world news classification task using just the free tier resources of Google Colab. You’ll learn how to leverage advanced techniques like LoRA and SFT to achieve a significant 3% performance boost with only 100 steps of fine-tuning. This tutorial is designed for anyone passionate about Large Language Models (LLMs) and eager to apply cutting-edge methods in the most efficient way possible.
Whether you’re a student, researcher, or just a curious mind, this video will give you the tools to enhance your AI models and achieve results that were previously only possible with expensive hardware. We’ll dive deep into the process, from loading the datasets to tweaking the finetuning script to suit your needs.
What You’ll Learn:
1) How to set up and fine-tune LLaMA 3.1 8B on Google Colab for free.
2) The steps to boost model performance by 3% with minimal resources.
3) Practical insights into LoRA and SFTTrainer for effective model training.
4) How to modify and adapt the finetuning script for your own datasets.
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