DAY 69 / 210

Intro to Parameter-Efficient LLM Fine-Tuning

This day launches the finetune phase by establishing core concepts before any code changes; it matters because StartupTribunal's Maku flows will later require targeted adaptation rather than full retraining to stay within rate limits and iteration speed.

⏱ 45 min target📝 3 quiz Qs

Resources

readingarXiv
LoRA: Low-Rank Adaptation of Large Language Models
Abstract + Sections 1-3
25 min
readingHugging Face
Fine-tuning a pretrained model
Quickstart through Trainer API
20 min

Deliverable

Journal entry committing a one-paragraph fine-tuning plan for BriefForm inference path

Quiz · 3 questions

1. Why does full fine-tuning often fail for production LLM apps under rate limits?

It always improves accuracyIt requires updating every parameter and inflates compute costIt never changes model behaviorIt only works on vision models

2. Name one key misconception when first applying LoRA to an existing route handler.

3. How would you measure whether a LoRA adapter on the brief route improves relevance without violating the rate limiter?

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)