DAY 52 / 210

Introduction to Parameter-Efficient Fine-Tuning

This first day of phase-2-finetune establishes the core concepts and tooling for adapting large models without full retraining. It sets the foundation for all subsequent days by connecting efficiency techniques directly to production constraints faced in StartupTribunal-style deployments.

⏱ 45 min target📝 2 quiz Qs

Resources

readingarXiv
LoRA: Low-Rank Adaptation of Large Language Models
abstract + sections 1-3
20 min
readingHugging Face
PEFT: Parameter-Efficient Fine-Tuning
overview + quickstart
15 min

Deliverable

Journal entry listing three PEFT methods and one concrete use-case for the current app/maku codebase

Quiz · 2 questions

1. Which statement about full fine-tuning versus LoRA is correct?

LoRA updates all model weightsFull fine-tuning always uses less memory than LoRALoRA injects low-rank matrices while freezing base weightsLoRA requires a separate optimizer for every layer

2. Name one common misconception when first applying PEFT libraries to an existing training script.

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)