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The AI Pilot · № 16

Fine-Tuner

Fine-tuned an open-source model on a custom dataset, measured performance gains over the base model, and documented methodology.

The idea

Fine-tuning works only if you can prove it worked. Have your kid set up the eval before they train: baseline performance, target performance, the same test set. Most fine-tunes are vibes-based and the kid can't tell if anything improved over the base model. The eval is what makes the work real. Without it, the kid has burned compute and learned nothing measurable. With it, they have an answer.

Steps
  1. Pick the base model and dataset.
  2. Set up the eval first: baseline vs. target, same test set.
  3. Fine-tune. Measure against baseline.
  4. Document methodology and gains.
What counts

A fine-tuning run with measurable improvement over baseline. The methodology writeup and results are plenty.