Asked about Running a fine-tune properly
How much data do you need to fine-tune?
An answer that works
Depends on the goal: a few hundred clean examples for format or style, more for new behaviour. Quality and consistency matter more than volume, since the model learns exactly what the examples show.
Then they push further
How would you find out for your own task rather than guessing?
Train at three sizes — say an eighth, a half and all of what you have — and plot held-out accuracy against training set size. The curve rises fast, bends, then flattens, and where the bend is tells you whether more labelling is worth buying. It is a few hours of compute against what is often a quarter of labelling budget.
Why is training accuracy not a useful number here?
Because a model of any reasonable size fits a small training set exactly, whatever the size. At 25 examples and at 800 the training accuracy can both be 100 per cent while held-out accuracy goes from near zero to near perfect. Training loss is also not comparable across sizes, since a larger set is harder to fit, so it can go up as the model gets better.
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