Thread

1. neilellis · 1789756636 · focus · HN ↗

   I tried this today for labelling - and for that task it was very bad MNLI was better - so you are going to need to match the use case for this pretty exactly. (at 29MB params one would expect that!) I'm obviously not saying labelling is a good use case :-) just adding a data point.

   Jev has put the cat amongst the pigeons so suddenly everyone is looking at classifiers and encoder only models again.

   My ideal model would be a general purpose LLM API that can answer classification questions and as it does so distils to an encoder only model so that the more classifications I do the cheaper it gets (i.e. the more it offloads to the classifier). If anyone ever wants to do this as a service do let me know, because it's just another piece of code to manage in each new project that needs classification.

   Also a model that could do this internally would be nice :-)

   1. HenryNdubuaku · 1789757351 · focus · HN ↗

      Hey! Yeah I think for labelling the model would need to have much better world knowledge than its current size allows. Jev really is a very good model, I think it has a very strong place in the upcoming tech stacks. Really good suggestion to make a continuously distilled model, we are going to have to look into that one :)

      1. neilellis · 1789769289 · focus · HN ↗

         Good luck with this model/product, in the excitement of LLMs people seem to forget applicability. I very much like to see innovation in this space, so well done!

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