There are some samples of training data in this folder - <a href="https://github.com/MakazhanAlpamys/Soup/tree/main/examples/data" rel="nofollow">https://github.com/MakazhanAlpamys/Soup/tree/main/examples/d...
They're all very short though. Anyone got a good rule for how much data of this nature is needed to successfully fine-tune a model of this size?
Those are format examples and test fixtures. Five to ten rows each. Not training data.
You did spot a real problem though. Eight configs in `examples/configs` pointed at those fixtures as training data. Seven were still on the old schema and would not even parse. I've fixed that, added a README to the folder, and a test that parses every config so it doesn't quietly break again.
Dataset size mostly depends on the task. Format or style, a few hundred examples is often enough. A task the model already half knows, usually a few thousand. New facts are often a bad fit for fine-tuning. I'd reach for RAG instead.
I haven't measured how this changes with model size, so I'm not going to make up a number.
simonw · · focus · HN ↗
They're all very short though. Anyone got a good rule for how much data of this nature is needed to successfully fine-tune a model of this size?
MakazhanAlpamys · · focus · HN ↗