Module 3: Parameter-Efficient Fine-Tuning (PEFT)
Efficient Fine-Tuning for Everyone
This module introduces Parameter-Efficient Fine-Tuning (PEFT), a family of methods that make fine-tuning accessible on consumer hardware while maintaining quality.
What You'll Learn
In this module, you will:
- Understand PEFT motivation and why it was developed
- Compare PEFT vs full fine-tuning to know when to use each
- Explore all major PEFT methods including LoRA, QLoRA, Prefix Tuning, and more
- Choose the right method for your specific use case
Module Structure
| Chapter | Topic | Duration |
|---|---|---|
| Chapter 1 | Introduction to PEFT | 25 min |
| Chapter 2 | PEFT vs Full Fine-Tuning | 25 min |
| Chapter 3 | PEFT Methods Overview | 30 min |
| Quiz | Module 3 Quiz | 30 min |
Key Concepts Preview
PEFT (Parameter-Efficient Fine-Tuning)
Methods that train only a small subset of parameters while keeping the base model frozen.
LoRA (Low-Rank Adaptation)
Adds trainable low-rank matrices to frozen weights, the most popular PEFT method.
Adapters
Small trainable modules inserted between frozen layers.
Prefix Tuning
Adds trainable "virtual tokens" as prefixes to the input.
Why This Matters
PEFT methods have democratized LLM fine-tuning:
- Accessibility: Train 7B+ models on consumer GPUs
- Cost Reduction: 90%+ memory savings
- Quality Retention: Comparable results to full fine-tuning
- Flexibility: Mix and swap adapters for different tasks
- Safety: No catastrophic forgetting of base knowledge
Prerequisites
Before starting this module, you should:
- Have completed Modules 1 and 2
- Understand why full fine-tuning is resource-intensive
- Be familiar with neural network concepts
- Know basic PyTorch operations
Let's Get Started!
Begin with Chapter 1: Introduction to PEFT.
Estimated time to complete this module: 2 hours