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- 0:49:04 Prompt Engineering vs. Fine-Tuning
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Classroom Contents
Introduction to Fine-Tuning Large Language Models
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- 1 - 0:00:00 Course Overview
- 2 - 0:00:15 What is Fine-Tuning and How is it Different?
- 3 - 0:00:21 Hands-on Methodologies
- 4 - 0:00:28 Deep Dive into Parameter Efficient Fine-Tuning
- 5 - 0:00:36 Exploring QLoRA: A Revolutionary Method
- 6 - 0:00:44 Practical Case Studies
- 7 - 0:01:15 Instructor Introduction
- 8 - 0:02:05 Course Outline More Detail
- 9 - 0:02:30 Highlight of the Course: Parameter Efficient Fine-Tuning
- 10 - 0:03:08 Who is this Course For?
- 11 - 0:04:01 Module 1: Introduction to Fine-Tuning
- 12 - 0:05:27 The Benefits of Fine-Tuning
- 13 - 0:07:17 First Part: Fine-Tuning LLMs Module
- 14 - 0:07:51 Fine-Tuning Allocation in LLM Life Cycle
- 15 - 0:08:43 Pre-trained vs Fine-Tuned Model
- 16 - 0:10:40 Understanding Shortcomings and Specialization
- 17 - 0:11:00 Fine-Tuning Impact Example: Chatbot
- 18 - 0:13:07 Formal Definition of Fine-Tuning
- 19 - 0:18:53 Fine-Tuning Examples: Doctor and Lawyer
- 20 - 0:26:08 Pre-Training vs. Fine-Tuning
- 21 - 0:49:04 Prompt Engineering vs. Fine-Tuning
- 22 - 0:55:00 Pros & Cons of Prompt Engineering vs. Fine-Tuning
- 23 - 1:01:41 Fine-Tuning Benefits & Demerits
- 24 - 1:07:13 Step-by-Step Fine-Tuning Process
