Fine-Tuning

In our Simple Guide to LoRA, we explored the theory behind Low-Rank Adaptation and how it slashes the GPU memory requirements for model training. Now, let’s put theory into practice. In this guide, we’ll walk through a step-by-step, hands-on tutorial to fine-tune Meta’s Llama 3 (8B) on a custom dataset using Hugging Face, PEFT, and QLoRA (Quantized LoRA) on a single GPU. Step 1: Format Your Dataset To train Llama 3, you need prompt-response pairs. Llama 3 uses a specific chat template format. For custom datasets, the easiest approach is to structure your data as a JSON file containing lists of messages: ...

Have you ever tried to download or fine-tune a modern Large Language Model (LLM) like Llama 3 or Mistral? If so, you probably ran into a massive wall: GPU memory. Fine-tuning models with billions of parameters requires specialized, high-end hardware, costing thousands of dollars. But what if you could achieve the exact same performance by training less than 1% of the model’s parameters? That is the magic of LoRA (Low-Rank Adaptation). It is currently the most popular technique for making fine-tuning fast, cheap, and accessible to everyone. In this guide, we’ll break down how it works, why it is so effective, and how you can use it. ...