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 maintaining performance.
Gain practical experience through hands-on exercises using quantization methods like int8 and bits and bytes. Delve into parameter-efficient fine-tuning (PEFT) techniques, focusing on implementing LoRA and QLoRA, which enable efficient fine-tuning using limited computational resources.
After completing this course, you’ll master LLM fine-tuning, PEFT fine-tuning, and advanced quantization parameters, equipping you with the expertise to adapt and optimize LLMs for various applications.
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
Show more
Content
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.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Developed by MAANG Engineers
Trusted by 2.8 million developers working at companies
"I really appreciated how the course clearly explained each concept in a step-by-step manner, and the hands-on coding exercises made it easy to experiment and deepen my understanding. I especially liked how each lesson was built upon the previous one."
Prathyush Babu
Senior Software Engineer @ PrivateCircle
"These are high-quality courses. Trust me. I own around 10 and the price is worth it for the content quality. EducativeInc came at the right time in my career. I'm understanding topics better than with any book or online video tutorial I've done. Truly made for developers. Thanks"
Anthony Walker
@_webarchitect_
"Just finished my first full #ML course: Machine learning for Software Engineers from Educative, Inc. ... Highly recommend!"
Evan Dunbar
ML Engineer
Hands-on Learning Powered by AI
See how Educative uses AI to make your learning more immersive than ever before.
AI Prompt
Code Feedback
Explain with AI
AI Code Mentor
Free Resources
