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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
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👾 jedi-or-sith
👾 Nattira Maneerat
👾 Justin Hual
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