DAY 74 / 210
Introduction to Parameter-Efficient Fine-Tuning
Phase 2 begins the shift from pretraining-scale work to targeted adaptation; today establishes the core motivation and taxonomy of PEFT methods so later days can implement and benchmark them against full fine-tuning baselines.
⏱ 45 min target📝 3 quiz Qs
Resources
- 20 min
- 15 minreadingHugging FacePEFT: Parameter-Efficient Fine-Tuning of Billion-Scale Models
Overview and Quicktour
Deliverable
Journal entry (1-2 paragraphs) comparing full fine-tuning vs. at least two PEFT methods with one concrete example from the learner's current stack
Quiz · 3 questions
1. Which PEFT method freezes the base model and injects trainable low-rank matrices into attention layers?
2. Why does LoRA typically reduce memory footprint more than Adapter layers during training?
3. In the context of a production API serving multiple users, list two deployment advantages of using a PEFT checkpoint versus a fully fine-tuned model.