DAY 51 / 210

Introduction to Parameter-Efficient Fine-Tuning

This day launches the finetune phase by establishing core concepts that will underpin all subsequent adaptation work. It positions efficient methods as the practical bridge between pretrained models and domain-specific deployment for builders like Maku. The focus ensures later days can iterate on real code rather than foundational theory.

⏱ 45 min target📝 3 quiz Qs

Resources

readingarXiv
LoRA: Low-Rank Adaptation of Large Language Models
Sections 1-3
20 min
readingHugging Face
PEFT: Parameter-Efficient Fine-Tuning
Overview and Quicktour
15 min

Deliverable

Journal entry with 300-word summary of LoRA mechanics plus one concrete use-case for StartupTribunal data

Quiz · 3 questions

1. Why does LoRA freeze the original weights instead of updating them directly?

To reduce GPU memory usage during trainingTo increase model size for better accuracyTo enable faster inference onlyTo avoid any form of gradient computation

2. Name one key limitation of full fine-tuning that PEFT methods directly address.

3. Describe a scenario where applying LoRA to a legal-domain model would be preferable to full fine-tuning for a startup builder.

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)