About

Ekcron is focused on one question: how do you get the quality of a large language model out of something far smaller and cheaper to run?

That pulls in a few connected topics — understanding how large models behave, fine-tuning open models for specific tasks, and distilling large models into small ones without losing what made them useful. The writing here works through those ideas in the open: what the methods are, where they break, and how you actually tell whether a smaller model is good enough to rely on.

It's early and hands-on rather than polished. Experiments, notes, and results go up as they happen. If any of it turns into something you can use directly, it'll appear here first.