Gain insights into fine-tuning LLMs with LoRA and QLoRA. Explore parameter-efficient methods, LLM quantization, and hands-on exercises to adapt AI models with minimal resources efficiently.

finetuning2.tar.gz

quantization

lora

qlora

exercise

This hands-on course will teach you the art of fine-tuning large language models (LLMs). You will also learn advanced techniques like Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA) to customize models such as Llama 3 for specific tasks. The course begins with fundamentals, exploring fine-tuning, the types of fine-tuning, comparison with pretraining, discussion on retrieval-augmented generation (RAG) vs. fine-tuning, and the importance of quantization for reducing model size while maintaining performance.

Gain practical experience through hands-on exercises using quantization methods like int8 and bits and bytes. Delve into parameter-efficient fine-tuning (PEFT) techniques, focusing on implementing LoRA and QLoRA, which enable efficient fine-tuning using limited computational resources. 

After completing this course, you’ll master LLM fine-tuning, PEFT fine-tuning, and advanced quantization parameters, equipping you with the expertise to adapt and optimize LLMs for various applications.

Fine-Tuning LLMs Using LoRA and QLoRA

Learn about the components and working of the Quantized Low-Rank Adaptation (QLoRA) technique.

Getting Started

Basics of Fine-Tuning

Exploring LoRA

Wrap Up

Quantized Low-Rank Adaptation (QLoRA)

Components of QLoRA

4-bit NormalFloat quantization