DAY 66 / 210

LoRA Fundamentals for Efficient Adaptation

This day launches phase-2-finetune by establishing why full fine-tuning is impractical for builders like Maku Mazakpe and introduces LoRA as the practical alternative. It creates the conceptual base for all later days that will apply these techniques directly to StartupTribunal workloads.

⏱ 45 min target📝 3 quiz Qs

Resources

readingarXiv
LoRA: Low-Rank Adaptation of Large Language Models
Abstract + Sections 1-3
25 min
readingHugging Face
PEFT: Parameter-Efficient Fine-Tuning
Full post
15 min

Deliverable

300-word journal entry posted in app/maku/page.tsx comments describing how LoRA would replace current inference path

Quiz · 3 questions

1. Why does LoRA reduce memory usage compared with full fine-tuning?

It freezes the base weights and only trains low-rank matricesIt prunes 90% of the model parametersIt converts all weights to 4-bit integersIt removes the optimizer entirely

2. Name one concrete risk of applying full fine-tuning to a startup-scale model like the one behind BriefForm.tsx.

3. How might the rate-limiter in lib/rate-limiter.ts interact with a newly fine-tuned model endpoint?

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)