Arcee AI's DistillKit provides tools for training a smaller model with guidance from a larger one. Its logit-based method teaches the smaller model to match the teacher's distribution of scores over possible outputs, alongside learning from the expected answers. That supplies information beyond a single target answer.
In this implementation, the two models must share a tokenizer, meaning they divide text into the same units. Arcee reports improvements over ordinary supervised training in its comparisons, but warns that its evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → results should not be compared directly with public leaderboard scores. The release supports experimenting with teacher guidance under a controlled comparison.
Terms in this piece · Glossary
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
alignment — The work of making AI systems actually pursue what their builders and users intend, rather than something subtly or dangerously different.
