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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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13 Lessons

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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Explanations

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Explanations

Course Overview

What You'll Learn

Course Content

Course Overview

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 exe...Show More

This course will cover fine-tuning techniques and parameter-efficient adaptations for AI models. It starts with the core concept...Show More

What You'll Learn

An understanding of the fundamentals of fine-tuning LLMs in machine learning and the various fine-tuning techniques

Familiarity with LLM quantization and its role in reducing model size for efficient deployment, including techniques like int8 quantization and bitsandbytes quantization

Hands-on experience implementing quantization techniques to optimize models

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

Hands-on experience fine-tuning the Llama 3 model using custom datasets

An understanding of the fundamentals of fine-tuning LLMs in machine learning and the various fine-tuning techniques

Course Content

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.

Low-Rank Adaptation (LoRA)

Hands-On LoRA

Quantized Low-Rank Adaptation (QLoRA)

Hands-On QLoRA

Quiz: Exploring LoRA

Wrap Up

2 Lessons

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

Exercise: Fine-Tuning LLMs Using LoRA

Conclusion

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