Learn about fine-tuning large language models (LLMs). The course covers key methodologies, including supervised fine-tuning and reinforcement learning with human feedback (RLHF). It also introduces a technique called QLoRA, which enables you to fine-tune massive models like Llama 70B on a home workstation. The course guides you from theory to practical implementation using tools like Python, PyTorch, and Hugging Face.

✏️ Created by ‪@LunarTech_ai‬

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⭐️ Contents ❤️
0:00:00 Course Overview
0:00:15 What is Fine-Tuning and How is it Different?
0:00:21 Hands-on Methodologies
0:00:28 Deep Dive into Parameter Efficient Fine-Tuning
0:00:36 Exploring QLoRA: A Revolutionary Method
0:00:44 Practical Case Studies
0:01:15 Instructor Introduction
0:02:05 Course Outline (More Detail)
0:02:30 Highlight of the Course: Parameter Efficient Fine-Tuning
0:03:08 Who is this Course For?
0:04:01 Module 1: Introduction to Fine-Tuning
0:05:27 The Benefits of Fine-Tuning
0:07:17 First Part: Fine-Tuning LLMs Module
0:07:51 Fine-Tuning Allocation in LLM Life Cycle
0:08:43 Pre-trained vs Fine-Tuned Model
0:10:40 Understanding Shortcomings and Specialization
0:11:00 Fine-Tuning Impact Example: Chatbot
0:13:07 Formal Definition of Fine-Tuning
0:18:53 Fine-Tuning Examples: Doctor and Lawyer
0:26:08 Pre-Training vs. Fine-Tuning
0:49:04 Prompt Engineering vs. Fine-Tuning
0:55:00 Pros & Cons of Prompt Engineering vs. Fine-Tuning
1:01:41 Fine-Tuning Benefits & Demerits
1:07:13 Step-by-Step Fine-Tuning Process

🎉 Thanks to our Champion and Sponsor supporters:
👾 Drake Milly
👾 Ulises Moralez
👾 Goddard Tan
👾 David MG
👾 Matthew Springman
👾 Claudio
👾 Oscar R.
👾 jedi-or-sith
👾 Nattira Maneerat
👾 Justin Hual

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