LoRA Fine-Tuning Lab

Overview

Almost every “AI project” out there consumes a hosted API. This one goes the other way: I’m training a model. The LoRA Fine-Tuning Lab takes a small open-weight model and adapts it to a focused task with Low-Rank Adaptation (LoRA) — the parameter-efficient technique that makes fine-tuning possible on a single consumer GPU — then measures whether the tuned model actually beats the base model on that task.

The problem

Prompting a general model gets you 80% of the way on most tasks, then plateaus. For narrow, structured jobs — consistent formatting, a specific tone, reliable extraction — a small fine-tuned model can be cheaper, faster, and more reliable than a giant general one. But that’s a claim you have to earn with numbers, not assert.

What I’m building

• A clean data pipeline — curating and formatting a task-specific dataset into instruction/response pairs, with a held-out test split kept honest.
• LoRA fine-tuning — training low-rank adapters with PEFT on a small base model, tracking loss and tuning rank, learning rate, and target modules.
• Before/after evaluation — scoring the base model vs. the tuned model on the same held-out set (scored through my LLM Eval Harness), so the improvement is measured, not claimed.
• A writeup — documenting what moved the needle and what didn’t, because the learning is the point.

Why it matters

This is the project that turns “familiar with fine-tuning (LoRA)” into something I can actually defend in an interview — the data work, the training loop, the evaluation, and the honest tradeoffs of small-tuned vs. large-general models.

Status

Actively building. Dataset curation and the training loop first, then the head-to-head eval and the writeup.