Fine-tuning helps you become a master and professional in AI engineering.
We just posted a course on the freeCodeCamp.org YouTube channel that offers a comprehensive guide to fine-tuning LLMs, taking you from the basics to advanced practical applications. It’s taught by Tatev, an expert with over seven years of experience in data science, data engineering, and AI engineering. She is also the CEO of Lunar Tech.
Throughout the course, you’ll learn the key differences between fine-tuning, pre-training, and prompt engineering, and dive into powerful methodologies such as supervised fine-tuning and reinforcement learning with human feedback (RLHF). The course also teaches about QLoRA, a revolutionary technique that allows you to fine-tune massive models like Llama 70B on a home workstation.
Here are the sections covered in this course:
What is Fine-Tuning and How is it Different?
Hands-on Methodologies
Deep Dive into Parameter Efficient Fine-Tuning
Exploring QLoRA: A Revolutionary Method
Practical Case Studies
Instructor Introduction
Course Outline (More Detail)
Highlight of the Course: Parameter Efficient Fine-Tuning
Who is this Course For?
Module 1: Introduction to Fine-Tuning
The Benefits of Fine-Tuning
First Part: Fine-Tuning LLMs Module
Fine-Tuning Allocation in LLM Life Cycle
Pre-trained vs Fine-Tuned Model
Understanding Shortcomings and Specialization
Fine-Tuning Impact Example: Chatbot
Formal Definition of Fine-Tuning
Fine-Tuning Examples: Doctor and Lawyer
Pre-Training vs. Fine-Tuning
Prompt Engineering vs. Fine-Tuning
Pros & Cons of Prompt Engineering vs. Fine-Tuning
Fine-Tuning Benefits & Demerits
Step-by-Step Fine-Tuning Process
Watch the full course on the freeCodeCamp.org YouTube channel (2-hour watch).
Source: freeCodeCamp Programming Tutorials: Python, JavaScript, Git & MoreÂ
