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by george_max4 hours ago |
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upvoteby jonmagic3 hours ago|
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I've been following jevbench twice a day for the past week and that's been a lot of fun. Latest update:

Rank System Score Public / sealed accuracy Evidence

1 decider-4b v2 64.13 83.5% / 34.7% Evaluator-run, offline

2 Jev 1.13 63.29 86.6% / 36.7% Evaluator-run API

3 JevK5 v0.2 62.04 85.3% / 33.1% Evaluator-run

4 Cygnet 12B 61.76 87.9% / 33.8% Evaluator-run, offline

5 Hopper 59.43 82.3% / 34.1% Evaluator-run

28 Kev 4B 36.14 66.2% / 22.4% Evaluator-run

41 Laya 421M 30.25 58.4% / 30.8% Evaluator-run

https://benchmarkheaven.com/jev-models

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upvoteby Havoc2 hours ago|
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Amazing - was looking for some benchmarks around this earlier
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upvoteby philipodonnell2 hours ago|
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What the best way to see how a homegrown version compares?
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upvoteby scronkfinkle4 hours ago|
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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.

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upvoteby mtkd3 hours ago|
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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

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upvoteby digitaltrees3 hours ago|
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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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upvoteby shepardrtc3 hours ago|
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It really does just work. And it works so well I already integrated it into my product. Saves me about 75% of costs for the section its working in, which isn't a small amount. I see a lot of negativity and I don't really get it either. Its so cheap and so fast, why not give it a try?
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upvoteby DenisM2 hours ago|
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I think it’s the infamous Dropbox reaction - anyone can wrap an FTP server, where the innovation?

Starting from a business POV one should inflate terminology, hack together an MVP, and see if the market demands it before doing hardcore R&D.

But starting from technical/craftsman POV all you see is a hack and a lot of big words, so it’s easy to become jaded.

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upvoteby ichorio42 minutes ago|
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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.

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upvoteby not_a_bot_4sho3 hours ago|
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I didn't see any negativity in the post you replied to.

I think the point being made is that Jev is great but it has no competitive moat, and open source versions will very soon catch up if their secret sauce is just synthetic data.

(Whether or not that is true, I don't know.)

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upvoteby cobanov4 hours ago|
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Developer here. You're right, Laya is a lot weaker than Jev, especially on harder queries. It's a small model, so it's fast, but that's the trade-off. The open models that get close to Jev are much bigger, and running those is what I'm working on next.
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upvoteby adinb1 hours ago|
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It doesn’t to be a ton bigger, 16k and reliable 8k would be a godsend. (I run at 2k)
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upvoteby mikodin3 hours ago|
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What are the models? I am super curious in these as well
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upvoteby simcop23872 hours ago|
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Probably Kev and/or the decider models. Kev is trained on one of the 4B qwen models, similar for decider but it ranges from 0.8B through to the 35B-A3B model so far I believe.
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upvoteby jasonjmcghee1 hours ago|
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In my experience it's not close and the benchmarks I've seen don't reflect my experience at all.

But I'm guessing people will find the right training regime and data mix soon to close the gap.

But big things I see are instability and inaccuracy - like pick a random problem.

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upvoteby verdverm3 hours ago|
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one day, perhaps people will click through to the laya author's arxiv paper content and the why may become clearer, you won't have to read it, a skim will suffice
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upvoteby iamflimflam13 hours ago|
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Nothing yet. Unfortunately it sometimes feels like our industry has been overrun by grifters and chancers.

I’m sure this has been a gradual and long decline. Maybe it even started with the dot com boom and accelerated with crypto. With AI it seems to have got worse.

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