Fine-Tuning LLMs Using LoRA and QLoRA

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.

Advanced

13 Lessons

2h

Certificate of Completion

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 has a GPU-powered coding session. Do I have access to it?
  • GPU-enabled code execution will let you query state of the art models on custom prompts
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Course Overview

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 maint...Show More

What You'll Learn

A solid foundation in fine-tuning LLMs, including practical techniques for Llama 3 fine-tuning and broader LLM fine-tuning workflows

Familiarity with LLM quantization methods, such as int8 quantization and bits and bytes quantization, for reducing model size and improving deployment efficiency

Hands-on experience implementing quantization techniques and optimizing models for performance and efficiency

An understanding of Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA) as key approaches for parameter-efficient fine-tuning (PEFT)

Hands-on experience fine-tuning Llama 3 model with custom datasets, using PEFT fine-tuning techniques for real-world applications

What You'll Learn

A solid foundation in fine-tuning LLMs, including practical techniques for Llama 3 fine-tuning and broader LLM fine-tuning workflows

Show more

Course Content

1.

Getting Started

Get familiar with fine-tuning LLMs using LoRA and QLoRA with practical insights.
2.

Basics of Fine-Tuning

Look at fine-tuning LLMs, types of fine-tuning, quantization, and hands-on quantization steps.
3.

Exploring LoRA

Go hands-on with parameter-efficient fine-tuning techniques like LoRA and QLoRA for LLMs.
4.

Wrap Up

Engage in resource-efficient fine-tuning methods and optimize LLMs for diverse applications.

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