In this course, you’ll learn LLM fine-tuning, including LoRA and QLoRA fine-tuning techniques, and apply LLM quantization to efficiently adapt large AI models like Llama 3 with hands-on exercises.
Advanced
13 Lessons
2h
Certificate of Completion
In this course, you’ll learn LLM fine-tuning, including LoRA and QLoRA fine-tuning techniques, and apply LLM quantization to efficiently adapt large AI models like Llama 3 with hands-on exercises.
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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...
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
What You'll Learn
An understanding of the fundamentals of fine-tuning LLMs in machine learning and the various fine-tuning techniques
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Course Content
Getting Started
Basics of Fine-Tuning
Exploring LoRA
Wrap Up
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