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.

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This course will cover fine-tuning techniques and parameter-efficient adaptations for AI models. It starts with the core concepts of LLM fine-tuning, including its importance in adapting pretrained models to specific tasks. You’ll explore various fine-tuning methods, such as parameter-efficient fine-tuning (PEFT), while understanding key comparisons like pretraining vs. fine-tuning and RAG vs. fine-tuning.

You’ll learn about LLM quantization and focus on int8 and bitsandbytes quantization with hands-on exercises. Moreover, you’ll delve into Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), which are useful for efficiently fine-tuning large models like Llama 3 on custom datasets. You’ll implement LoRA and QLoRA fine-tuning through interactive sessions, learning to adapt models with minimal resources.

By the end of this course, you’ll gain the expertise to fine-tune and optimize LLMs, becoming skilled in essential model adaptation techniques.

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)