DAY 65 / 210

LoRA Fundamentals for Efficient Fine-Tuning

This day launches the finetune phase by establishing why parameter-efficient methods are essential when adapting large models under real compute constraints. It directly supports the arc toward production-grade domain adaptation for tools like StartupTribunal by replacing naive full fine-tuning with targeted, low-resource techniques from the outset.

⏱ 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 Quickstart Guide
entire quickstart page
15 min

Deliverable

Commit a new branch 'day65-lora-intro' containing journal.md with a one-paragraph experiment plan for applying LoRA to a 7B model

Quiz · 2 questions

1. Which statement correctly describes the core benefit of LoRA over full fine-tuning?

LoRA updates all model weights simultaneously for faster convergenceLoRA freezes the base weights and injects trainable low-rank matricesLoRA requires doubling the GPU memory to store gradientsLoRA only works on decoder-only architectures

2. In one sentence, explain why a practitioner might still choose full fine-tuning over LoRA for a new legal-domain task.

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)