[richdougherty]

You can run them either for general purpose inference. You can also fine-tune them and get improved performance for specific use cases.

It's safe to assume they're worse at every task than larger models, so I wouldn't look at use cases in terms of what tasks they can do compared to larger models.

But what's good about them is they're smaller so they can run on smaller and cheaper hardware. So an example would be to fine-tune and then run on some sort of local user device rather than in the cloud. This might become more practical in the future as hardware improves.

[theLiminator]

Yeah, my point is moreso is are smaller models ever "smart" enough to perform useful tasks?

Perhaps for basic code completion and simple writing tasks?