DAY 61 / 210

Intro to Parameter-Efficient LLM Fine-Tuning

Phase 2 shifts focus from pre-training scale to targeted adaptation. Establishing LoRA fundamentals today creates the technical baseline for all subsequent fine-tuning experiments and prevents inefficient full-parameter updates later in the arc.

⏱ 50 min target📝 3 quiz Qs

Resources

readingarXiv
LoRA: Low-Rank Adaptation of Large Language Models
Abstract + Sections 1-2
15 min
readingHugging Face
PEFT Quickstart
Full quickstart page
25 min

Deliverable

Journal entry containing a 3-bullet fine-tuning plan for a StartupTribunal model component

Quiz · 3 questions

1. What is the primary memory-saving mechanism in LoRA?

Freezing base weights and training low-rank adaptersQuantizing all layers to 4-bitPruning attention headsIncreasing batch size

2. Name one risk of full-parameter fine-tuning that LoRA avoids.

3. How might the rate-limiter pattern in the current codebase influence batch sizing decisions during fine-tuning?

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)