fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
A published method for training a small merge model end to end - data curation, error categories, inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → technique - arriving at a model that merges above 10k tok/s while cutting production merge errors.