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

pip3 install transformers==4.44.1
pip3 install accelerate
pip3 install bitsandbytes==0.43.3
pip3 install datasets==2.21.0
pip3 install trl==0.9.6
pip3 install peft==0.12.0
!pip install -U "huggingface_hub[cli]"

!huggingface-cli login --token "Enter your token" --add-to-git-credential

Learn how to fine-tune LLM on a custom dataset using LoRA. 

Getting Started

Basics of Fine-Tuning

Exploring LoRA

Wrap Up

Hands-On LoRA

Install the dependencies

Hugging Face CLI