Fine-Tune Llama 3.1 Ultra-Efficiently with Unsloth
The recent release of Llama 3.1 offers models with an incredible level of performance, closing the gap between closed-source and open-weight models. Instead of using frozen, general-purpose LLMs like GPT-4o and Claude 3.5, you can fine-tune Llama 3.1 for your specific use cases to achieve better performance and customizability at a lower cost.
In this article, we will provide a comprehensive overview of supervised fine-tuning. We will compare it to prompt engineering to understand when it makes sense to use it, detail the main techniques with their pros and cons, and introduce major concepts, such as LoRA hyperparameters, storage formats, and chat templates. Finally, we will implement it in practice by fine-tuning Llama 3.1 8B in Google Colab with state-of-the-art optimization using Unsloth.
All the code used in this article is available on Google Colab and in the LLM Course.
