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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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
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Course Content
Getting Started
Basics of Fine-Tuning
Exploring LoRA
Wrap Up
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