Yes, at this size (2B) you can get better performance and calibration fine tuning on your questions. You can also improve calibration on your problem type just be recalibrating without fine-tuning.
As to what's doable, that depends on what you expect. It's a single pass through a 2B model, no reasoning, so it's never going to be particularly 'intelligent'. On domain problems, though, you can get great in-distribution performance (and possibly better in-distribution performance than you'd get using the same volume of data to train a specialized classifier).
Everything you need to fine-tune, or even retrain from scratch, is in the github repo.
Yes, at this size (2B) you can get better performance and calibration fine tuning on your questions. You can also improve calibration on your problem type just be recalibrating without fine-tuning.
As to what's doable, that depends on what you expect. It's a single pass through a 2B model, no reasoning, so it's never going to be particularly 'intelligent'. On domain problems, though, you can get great in-distribution performance (and possibly better in-distribution performance than you'd get using the same volume of data to train a specialized classifier).
Everything you need to fine-tune, or even retrain from scratch, is in the github repo.