Fine-tuning Pipeline

Off-the-shelf models are generalists. The interesting work starts when you need one to be specifically good at your problem, your vocabulary, your edge cases — reliably, and more than once.

Context

Domain tasks punish generic models: the jargon is wrong, the formats drift, the edge cases pile up. Fine-tuning fixes that, but a one-off fine-tune is a science experiment, not an asset. The real need is a repeatable path from “we have examples” to “we have a better model”, one that survives the second and third iteration.

What I built

A fine-tuning pipeline that treats the whole loop as one system: dataset curation and versioning, training runs with tracked configurations, and evaluation harnesses that compare candidates against the incumbent on the cases that actually matter. Re-running it is a command, not a project.

Impact

Custom models stopped being one-off experiments and became something the product can depend on — improved on a cadence, measured against real tasks, and reproducible when it counts.