Module 2: Full Fine-Tuning
Understanding Traditional Fine-Tuning
This module covers the traditional approach to fine-tuning where all model parameters are updated during training. While more resource-intensive, understanding full fine-tuning is essential for grasping why PEFT methods were developed.
What You'll Learn
In this module, you will:
- Understand traditional fine-tuning and how it works
- Calculate computational requirements for different model sizes
- Learn about catastrophic forgetting and mitigation strategies
- Know when full fine-tuning is still the right choice
Module Structure
| Chapter | Topic | Duration |
|---|---|---|
| Chapter 1 | Traditional Fine-Tuning Approach | 25 min |
| Chapter 2 | Computational Requirements | 30 min |
| Chapter 3 | Catastrophic Forgetting | 25 min |
| Quiz | Module 2 Quiz | 30 min |
Key Concepts Preview
Full Fine-Tuning
Updating all model parameters during training, providing maximum customization potential.
GPU Memory
The VRAM required to store model weights, gradients, optimizer states, and activations.
Catastrophic Forgetting
Loss of previously learned knowledge when training on new data.
Gradient Accumulation
Simulating larger batch sizes by accumulating gradients over multiple forward passes.
Why This Matters
Full fine-tuning represents the upper bound of what's achievable through training. Understanding its requirements and limitations helps you:
- Appreciate why PEFT methods were developed
- Make informed decisions about when full fine-tuning is worth the cost
- Properly plan compute resources for your projects
- Avoid common pitfalls like catastrophic forgetting
Prerequisites
Before starting this module, you should:
- Have completed Module 1
- Understand basic neural network concepts
- Be familiar with GPU/VRAM concepts
- Have basic Python and PyTorch knowledge
Let's Get Started!
Begin with Chapter 1: Traditional Fine-Tuning Approach.
Estimated time to complete this module: 2 hours