Mlx

By the end of this article you will have trained a language model to do something it currently cannot do, on your own Mac, in about twenty seconds of compute. You will hand it a bug report in plain English and it will answer with exactly one line: severity=high component=auth summary=login fails after the token refresh runs The base model does not do this. Asked the same question it starts explaining token refresh and never reaches a parseable answer. You will measure that difference rather than eyeball it: twenty test reports held out of training so the model has never seen them, which the base model scores 0/20 on and your fine-tuned model scores 20/20 on, run by a script you write in Step 8. ...