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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.

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Overview

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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.

This hands-on course will teach you the art of fine-tuning large language models (LLMs). You will also learn advanced techniques...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

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

Content

13 Lessons2 Quizzes

Getting Started

1 Lessons

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

Course Overview

Basics of Fine-Tuning

5 Lessons

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

What Is Fine-Tuning?

Types of Fine-Tuning

Quantization: Reducing the Model Size

Hands-On Quantization

Quiz: Basics of Fine-Tuning

Exploring LoRA

5 Lessons

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

Wrap Up

2 Lessons

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

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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