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. ichorio · 1790375428 · focus · HN ↗

            If you don't mind me asking, what are you using it for?

            I've been unable to find a good use case for now.

            1. taylorfinley · 1790391308 · focus · HN ↗

               You should try building something with it, the hype is what it is, but the model is crazy useful.

               I'm building a woodworking app and I've managed to create an autopilot that can take a simple instruction ("get me 5 2x4s", "cut the middle 2x4 into 4 equal pieces", "move the 2x4 3 feet left") and the action instantly happens with next to no lag. There is already an llm but now it can share an intent, and the geometry system shows jev the various actions and jev chooses the action that gets it closer to the goal until it has found a state that matches the intent or gives up. The result is the llm can think "higher level" and let the cheap fast model grind out the options in a relative blink of the eye, without the 30s of reasoning the llm would have done about the various operations it could try.

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