Module 9Transformer Deep Dive
Fine-tuning and PEFT
Adapt pretrained models with full fine-tuning or parameter-efficient methods like LoRA.
Why this module matters
In practice, most teams adapt models rather than pretrain from scratch.
Prerequisites
- ▸ Pretrained model basics
Learning objectives
- ▸ Compare full fine-tuning and LoRA
- ▸ Understand rank and target-module choice
- ▸ Evaluate adaptation quality fairly
Core concepts
PEFT
LoRA rank
Task adaptation
Hands-on practice
- ▸ Apply LoRA to a small transformer classifier
Expected output
A fair comparison between full tuning and PEFT.
Study checklist
- ✅ Compare full fine-tuning and LoRA
- ✅ Understand rank and target-module choice
- ✅ Evaluate adaptation quality fairly
Common mistakes
- ⚠️ Treating LoRA as universally effective
- ⚠️ Ignoring base-model mismatch
- ⚠️ Comparing runs with different tokenizers or prompts
Module rhythm
- 1. Read the summary and why-it-matters section first.
- 2. Work through concepts before rushing into practice.
- 3. Use the checklist to verify real understanding, not just completion.
How to use this page well
Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.