DAY 72 / 210

Foundations of LLM Supervised Fine-Tuning

This day launches the finetune phase by establishing core concepts before any code changes, ensuring the learner understands parameter-efficient methods that will later be applied to StartupTribunal features. It matters because early misconceptions about data, loss, and adapters compound across the remaining weeks of phase-2.

⏱ 45 min target📝 3 quiz Qs

Resources

readingHugging Face
Fine-Tuning a Pretrained Model
Sections 1-3
20 min
readingarXiv
LoRA: Low-Rank Adaptation of Large Language Models
Abstract and Section 2
15 min

Deliverable

Journal entry with 200-word summary of SFT vs PEFT plus one concrete example of a training loop skeleton

Quiz · 3 questions

1. Which statement correctly distinguishes full fine-tuning from LoRA?

Full fine-tuning updates only the embedding layerLoRA freezes base weights and trains low-rank adaptersBoth methods require identical GPU memoryLoRA always yields lower final accuracy

2. Name one common misconception when beginners first prepare a dataset for supervised fine-tuning.

3. Explain in two sentences why learning-rate choice interacts differently with LoRA than with full fine-tuning.

Journal

Time spent (minutes)

Blockers

Commit / PR links (one per line)