DAY 44 / 210

Parameter-Efficient Finetuning with LoRA

This day introduces PEFT techniques that enable practical model adaptation under startup compute constraints. It directly supports later implementation work on StartupTribunal by replacing naive full fine-tuning patterns. Learners will measure efficiency gains against existing app routes handling model calls.

⏱ 50 min target📝 3 quiz Qs

Resources

readingarXiv
LoRA: Low-Rank Adaptation of Large Language Models
Sections 1-3 and 4.1
25 min
readingHugging Face
PEFT: Parameter-Efficient Fine-Tuning
Quicktour and LoRA sections
20 min

Deliverable

Journal entry with 200-word summary plus one concrete LoRA config snippet tested on a toy model

Quiz · 3 questions

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

It freezes the base model and trains only low-rank adaptersIt prunes weights below a thresholdIt quantizes all layers to 4-bitIt replaces the optimizer with SGD

2. Name one risk of applying LoRA without validation on domain-specific data.

3. How might the rate-limiter in the current API affect a fine-tuning job schedule?

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)