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Fine-Tuning LLMs Using LoRA and QLoRA
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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

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
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

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Content

1.

Getting Started

1 Lessons

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

2.

Basics of Fine-Tuning

5 Lessons

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

3.

Exploring LoRA

5 Lessons

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

4.

Wrap Up

2 Lessons

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