Updated 27 Aug
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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.
4.7
13 lessons
2h
Created by Educative
What's included
- 13 lessons across 4 modules
- 2 quizzes with instant feedback
- AI Tutor on every lesson
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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
Learning Roadmap
2.
Basics of Fine-Tuning
Basics of Fine-Tuning
Look at fine-tuning LLMs, types of fine-tuning, quantization, and hands-on quantization steps.
3.
Exploring LoRA
Exploring LoRA
5 Lessons
5 Lessons
Go hands-on with parameter-efficient fine-tuning techniques like LoRA and QLoRA for LLMs.
4.
Wrap Up
Wrap Up
2 Lessons
2 Lessons
Engage in resource-efficient fine-tuning methods and optimize LLMs for diverse applications.
Certificate of Completion
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Prathyush Babu
Senior Software Engineer @ PrivateCircle
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Anthony Walker
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Evan Dunbar
ML Engineer
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