Thread

1. george_max · 1790362784 · focus · HN ↗

   Has anyone actually seen better or the same results with Laya compared to Jev? From my experience, Laya performs significantly worse. It's less confident and often makes wrong decisions with more complex queries.

   1. scronkfinkle · 1790363124 · focus · HN ↗

      Yes. JEV generalizes better because they probably have an enormous corpus and trained on it for a long time. Laya's out of the box model is much weaker. However, in the age of LLM's it's incredibly easy and cheap to generate large datasets to fine tune laya for your task, and the training loop is pretty quick and cheap too.

      It's so easy that I question why I would ever pay for JEV when eventually I'll have done enough random things that I will also have a large corpus and likely a general model as well.

      1. mtkd · 1790365081 · focus · HN ↗

         Isn't the point of Jev that it generalises better?

         It's a fast classifier you can use out-the-box, ~1.5bn tokens is about $40 (I've been hammering it)

         It just works ... a whole bunch of low-level/low-importance workflow stuff that was getting farmed out to small/fast LLM models now has a competitive alternative ... and bits that hadn't even been considered to go into some external descision/classifier service can be tested/deployed at ~$0.00003/req

         I don't get this wall of negativity on it, it's genuinely innovative/useful tech ... would expect HN to be more positive, regardless of whether it's the absolute best execution

         1. digitaltrees · 1790366716 · focus · HN ↗

            I think your point is valid but many are annoyed that it is presented as groundbreaking, revolutionary, novel frontier tech when it is a known classification system. It’s the hype that feels undeserved. Honestly it was one of the best marketing campaigns I’ve seen.

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