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Comment by Zetaphor | Hacker News Reader

I'd argue that a tiny fine-tuned Jev on my dataset that I can run for practically free is far more valuable if the idea is to make this a dependency in real work

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People have been making their own calibrated classifiers and decision makers for a very long time. Mine outperform jev. But that's not the point of jev.

What is...?

i guess it's not needing to finetune for each specific use case

That is indeed reasonable. But you still need a bunch of trusted data to validate the accuracy of Jev.

Yes. The difference is you can immediately benchmark and iterate via prompting without having to retrain. It's such a time saver.

Is it though? I have found it much easier to fit/diagnose/improve simple classifiers on embeddings/hash vectorized features/etc than iterate on prompts, especially true when using the more modern LLM's (like gpt 5.6 Luna) where you can't even set the temperature to get any sense of determinism.

I do note though, that is infinitely easier to 'deploy' a Jev/LLM based solution than a data/model pipeline.

The comparison i'm making is against a training of encoder/small decoder model so I think we agree on most things. I dont think jev replaces the benefit of embeddings nor think they are mutually exclusive. All part of a handy utility belt.

Then the probabilities are not calibrated. Jev is basically the LLM version of the "is-even" library.

Being ZERO-SHOT. No fine tuning or re-training needed. Why are people so dense over this model?

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