[matusp]

Fine-tuning is a good technique to have in a toolbox, but in reality, it is feasible only in some use cases. On one hand, many NLP tasks are already easy enough for LLMs to have near perfect accuracy and fine tuning is not needed. On the other hand, really complex tasks are really difficult to fine-tune and clevem data collection might be pretty expensive. Fine-tuning can help with the use cases somewhere in the middle, not too simple, not too complex, feasible for data collection, etc.

[coldtea]

>Fine-tuning is a good technique to have in a toolbox, but in reality, it is feasible only in some use cases.

Yes, 100s of housands of them

[brulard]

Care to elaborate what are some of those use cases?

[coldtea]

Almost everything, isn't it?

From fine-tuning for coding assist, to medical applications, customer support, legal and financial use cases, various classification tasks, for government work, statistics, language learning, music, education, even for role playing game character AI...

I'd rather have a fine-tuned model specialized to any of those tasks and countless others when I'm doing one of those tasks, than a jack of all trades...

[libraryofbabel]

What would you say is an example of one of those “middle” tasks it can help with?

[CaptainOfCoit]

An example I just found worked very well with fine-tuning: I wanted to extract any frame that contained a full-screen presentation slide from a various videos I've archived, only when it's full-screen, and also not capture videos, and some other constraints.

Naturally I reached for CLIP+ViT which got me a ~60% success rate out of the box. Then based on that, I created a tiny training script that read `dataset/{slide,no_slide}` and trained a new head based on that. After adding ~100 samples of each, the success rate landed at 95% which was good enough to call it done, and circle back to iterate once I have more data.

I ended up with a 2.2K large "head_weights.safetensors" that increased the accuracy by ~35% which felt really nice.