DAY 46 / 210

Introduction to Parameter-Efficient Fine-Tuning

This day launches phase-2-finetune by establishing core concepts of adapting pretrained models without full retraining. It sets the foundation for later days that will apply these techniques to the user's own codebase and product workflows.

⏱ 50 min target📝 3 quiz Qs

Resources

readingHugging Face
PEFT: Parameter-Efficient Fine-Tuning of Billion-Scale Models
full post
20 min
readingHugging Face
Fine-Tuning a Pretrained Model
sections 1-3
25 min

Deliverable

journal entry documenting one PEFT method and its relevance to StartupTribunal

Quiz · 3 questions

1. Which statement best describes the main advantage of LoRA over full fine-tuning?

It requires no training dataIt updates only a small set of low-rank matrices while freezing the base modelIt always produces higher accuracy than full fine-tuningIt works exclusively with decoder-only models

2. Explain in one sentence why catastrophic forgetting is less likely with PEFT methods than with full fine-tuning.

3. Describe one potential drawback of using adapters like LoRA when the downstream task distribution differs significantly from pretraining data.

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)