Module 11 – Prompting, PEFT, and Quantization (Gemma)
What you'll learn
Builds on Modules 1–10
≈7 h · 2 h video · 3 h reading · 2 h coding
- Choose zero-shot, few-shot, or chain-of-thought prompting for a concrete task
- Explain how RAG grounds generation and when retrieval is preferable to changing weights
- Compare full fine-tuning with LoRA/QLoRA in trainable parameters and memory use
- Distinguish instruction tuning, preference learning, and policy optimization as alignment objectives
- Use the course decision framework to choose prompting, RAG, PEFT, or quantization
11.1 – ICL, Prompt Engineering, and RAG
11.2 – Parameter-Efficient Fine-Tuning (PEFT)
Slide preview
Suggested reading
11.3 – Quantization & QLoRA
📚 Resources & Lecture Code
🧪 Gemma Fine-Tuning
Gemma model card: ai.google.dev/gemma/docs/core/model_card_3