DAY 48 / 210

LoRA Fundamentals for Efficient Adaptation

This day launches the finetune phase by establishing parameter-efficient methods as the practical path from base models to domain-specific behavior. It matters because StartupTribunal workloads require targeted adaptation without full retraining cost or infrastructure.

⏱ 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 Library Overview
main concepts and quickstart
15 min

Deliverable

journal entry summarizing LoRA rank/ablation trade-offs with one concrete config for a 7B model

Quiz · 2 questions

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

To reduce GPU memory during trainingTo avoid catastrophic forgetting entirelyBecause full fine-tuning always divergesTo guarantee higher final accuracy

2. Name one practical downside of choosing a very low LoRA rank (r=4) on a domain-specific task.

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)