Guys I have a real q, what is the difference between an instruct based re-ranker and laya/jev I just don't see it.
Edit: One is that jev/laya are tuned to have better probabilities, but a reranker can be fine tuned to do that as well. And jev/laya use RLCD?
Swizec 1 hours ago [-]
> difference between an instruct based re-ranker and laya/jev I just don't see it
Main difference is that laya/jev/et-al give you a zero-shot classifier that requires no training. You can prompt engineer your way to a quick fairly reliable cheap enough decision engine that you can use to iterate quickly (by prompt engineering).
Right now a lot of people are doing this with LLMs and it's too slow and expensive.
Imo the right iterative approach to productionizing these systems is something like:
1. Build it with an LLM. Iterate on the prompt
2. Start building a real-world dataset
3. When the prompt works, turn it into a clear rubric for Jev or similar
4. Keep iterating until desired accuracy achieved
5. Use the real-world evals you've built to train a custom classifier fine-tuned to your needs
You now have a system that has produced useful results in production from the very beginning and by the end it's a reliable super cheap classifier that can make thousands of decisions per second.
rosegroove 7 minutes ago [-]
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avereveard 1 hours ago [-]
Calibrated probability across multi task with zero shot I guess. A reranker is single task and tuning it make it even more narrow. And I guess some piping to make multiclass efficient since you cannot mask logprob for independent questions in the same output space without throwing calibration away.
pradn 31 minutes ago [-]
I'm not sure what this means for AI startups if their innovations can be copied by OSS so quickly (what, like 2 weeks?). There's "consumer surplus" for everyone, to borrow an economic concept. But we do ideally want some of the surplus to flow to the innovator, too. I know there were precursors, but that's fine - it's hard to have a totally novel idea in such a popular field. I don't know what the end game is for TypeSafe - they'd need to demonstrate perpetually better results, or compete in another axis: UX, support, custom solutions, etc. So much of the time, someone proving a concept, or it simply getting enough publicity, is enough for a "Cambrian explosion" of follow-ups and copies. Famously, that was true for "Attention is All You Need", and the general idea of "next-token prediction" being so powerful.
We've stumbled into general differentiable models..
Presumably the training recipe and training dataset itself cannot be easily copied in a week or two. So if they want to shut down these competitor models they need to make it obvious how they are better than them.
rosegroove 9 minutes ago [-]
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george_max 3 hours ago [-]
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.
jasonjmcghee 1 minutes ago [-]
In my experience it's not even close and the benchmarks I've seen don't reflect my experience at all.
jonmagic 2 hours ago [-]
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
Amazing - was looking for some benchmarks around this earlier
philipodonnell 2 hours ago [-]
What the best way to see how a homegrown version compares?
scronkfinkle 3 hours ago [-]
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.
mtkd 2 hours ago [-]
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
digitaltrees 2 hours ago [-]
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.
shepardrtc 2 hours ago [-]
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?
DenisM 2 hours ago [-]
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.
not_a_bot_4sho 2 hours ago [-]
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.)
cobanov 3 hours ago [-]
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.
mikodin 2 hours ago [-]
What are the models? I am super curious in these as well
simcop2387 1 hours ago [-]
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.
verdverm 2 hours ago [-]
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
iamflimflam1 2 hours ago [-]
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.
nacs 23 minutes ago [-]
It would be good to list 1) zero-shot accuracy and 2) latency on the models page . The LLM-based models' latency is probably much higher than the BERT approaches I would assume.
Also curious, it seems from looking at the accuracy scores you gave that it seems to be NLI > Gliclass > Laya (for Bert types)? Why do you seem to feature/recommend Laya more - is Laya better in some way?
nickstinemates 24 minutes ago [-]
Laya is pretty easy to set up on its own without ollaya. I just did that and replaced my current jev API usage to laya running on a GTX 970 with 4GB of vram.
Very small context window, but for some existing small llm work I was doing, it was a drop-in replacement and it makes me happy I can get use out of old hardware I have running.
ranyume 3 hours ago [-]
>Run decision models locally.
>example is a text classification task instead of a decision
hbrn 2 hours ago [-]
"Decision model" is just marketing jargon.
decision model = classifier
system one model = small non-reasoning LLM
noul = boolean
confidence = f(probabilities)
It's sad to see how gullible engineers are today.
verdverm 1 hours ago [-]
> how gullible ... today
that laya is even a thing is further evidence, people took that author at face value, the paper contents are incomplete and describe something that does not sound like Jev at all
this was the period of arxiv history that led to the new vouching system, laya author contributed to that imo
hbrn 32 minutes ago [-]
My understanding is that Laya (or whatever it was called in 2025) was yet another fine-tuned classifier, not a general purpose one.
That said, Typesafe false marketing caused Laya to fit perfectly into pretty much every advantage that they are claiming: "system one decision model", cheap, fast, no hallucinations, structured, confidence output, parallel, calibrated. Their BS is their own demise.
I think Laya's author genuinely bought their BS and thinks he built the same thing. Unlike Typesafe, I don't think he's intentionally misleading people.
The only unique thing about Jev is that it's a general purpose classifier. Funny enough, they were so busy spreading marketing bullshit that they forgot to mention the only real thing that makes Jev unique.
verdverm 28 minutes ago [-]
Laya author is spitting more BS than Typesafe, the (incomplete) papers are nothing like Jev, they use RAG and azure hosted services for calculating embeddings, with an orchestrator. Jev is just a model, Laya was put together after Jev, almost certainly based on what the author learned from Typesafe, and then backported "his" idea
I suspect most people only read the blog post, and thought it was great how a VC company "stole" an idea and was "outdone" by a rando... without actually checking the facts. Confirmational reading bias, we live in a post-truth world with dysfunction media ecosystem
hbrn 4 minutes ago [-]
I think you're right about Laya (and confirmation bias).
But like you said, at the end of the day he's just a rando.
He's not asking for $40m, not saying "I made ChatGPT, but i hate it, so I built the next big thing". Not claiming to co-invent RLHF.
Laya is just noise. Jev's bullshit affects me today - I see people injecting it into the codebases where it has no place.
Their marketing language is misleading. They must still use some transformer language model backbone to encode the text input (BERT or decoder-only LLM). The biggest difference is the output, instead of auto-regressively generating tokens, they produce probabilities over a bounded set of decisions (more flexible classification).
1 hours ago [-]
cobanov 3 hours ago [-]
Fair point, that example is basically classification. I'll change it to something that looks more like a real decision.
mococa 2 hours ago [-]
It would be really cool to have LLMs and System One in a single tool - in this case, if Ollama implemented it.
verdverm 11 minutes ago [-]
next vLLM release will have this
if you use gateways, GoModel support the S1 endpoints, my favorite feature is the virtual models, stable name, I can swap out the backing model(s)
Would be great if you supported CUDA 12; I don't feel like paying $15K to upgrade my GPU right now
verdverm 12 minutes ago [-]
wait another week or so for vLLM's next release
handfuloflight 3 hours ago [-]
Sounds good on latency but how is its actual decision quality vs. Jev?
cobanov 3 hours ago [-]
Depends on the model. The small ones I support today are well below Jev on harder queries, but fine for simple, well-defined questions. The open models that get close to Jev are bigger, and I'm adding support for those next.
datadrivenangel 3 hours ago [-]
Are there many models that are comparable to Jev for generic decision making?
Smarter move if you have an eval set is to just train a classifier and call it a day.
<<<"i was curious to see if i could train a competitive Jev-like model completely autonomously with a swarm of agents using our internal system."
Bro is writing off the H200 lol
On a sidenote I really can't stand the term "swarm" and definately plays into AI doomerism.
lirolero 2 hours ago [-]
[dead]
cobanov 3 hours ago [-]
The link rgbrgb posted is a good overview. The best open ones are close to Jev now, but they're big models. And I agree, if you have an eval set for a fixed task, a trained classifier is the better choice.
vorticalbox 2 hours ago [-]
Does anyone know what laya multi lang is faster than laya en? I would have thought focusing on a single language would be faster.
emmettbt 3 hours ago [-]
Cool... but this does seem undermined by the fact that Ollama can add support for decision models at any time.
cobanov 3 hours ago [-]
Fair, and I'd be happy if they did. Ollaya uses the same API as Jev, so your code isn't tied to it either way
3 hours ago [-]
accountrequired 3 hours ago [-]
and that ollama is go-llama and not rust, so it's not really the ollama of anything
gauravsapkotanp 2 hours ago [-]
I have also tried this and its really awesome
george_max 3 hours ago [-]
I am fairly confident if Jev-style decision models are seen as prominent (which, they seem to be), Ollama will support them. Surprised the team hasn't implemented this already.
eserozvataf 3 hours ago [-]
great project for empowering open-source alternatives.
rkovashikawa 3 hours ago [-]
open-source is the only way for safe AI development. whoever doesn’t share the weights/code will lag behind.
cobanov 3 hours ago [-]
Thanks!
imnotr0b0t 2 hours ago [-]
[dead]
adityamwagh 2 hours ago [-]
Hey Claude, make ollama for Jev like models. Make no mistakes /s
verdverm 2 hours ago [-]
hey Claude, download and run vllm nightly for me
(already merged)
GoModel (gateway) already supports Jev like endpoints too
Edit: One is that jev/laya are tuned to have better probabilities, but a reranker can be fine tuned to do that as well. And jev/laya use RLCD?
Main difference is that laya/jev/et-al give you a zero-shot classifier that requires no training. You can prompt engineer your way to a quick fairly reliable cheap enough decision engine that you can use to iterate quickly (by prompt engineering).
Right now a lot of people are doing this with LLMs and it's too slow and expensive.
Imo the right iterative approach to productionizing these systems is something like:
You now have a system that has produced useful results in production from the very beginning and by the end it's a reliable super cheap classifier that can make thousands of decisions per second.We've stumbled into general differentiable models..
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
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.
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
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.
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.)
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.
Also curious, it seems from looking at the accuracy scores you gave that it seems to be NLI > Gliclass > Laya (for Bert types)? Why do you seem to feature/recommend Laya more - is Laya better in some way?
Very small context window, but for some existing small llm work I was doing, it was a drop-in replacement and it makes me happy I can get use out of old hardware I have running.
>example is a text classification task instead of a decision
decision model = classifier
system one model = small non-reasoning LLM
noul = boolean
confidence = f(probabilities)
It's sad to see how gullible engineers are today.
that laya is even a thing is further evidence, people took that author at face value, the paper contents are incomplete and describe something that does not sound like Jev at all
this was the period of arxiv history that led to the new vouching system, laya author contributed to that imo
That said, Typesafe false marketing caused Laya to fit perfectly into pretty much every advantage that they are claiming: "system one decision model", cheap, fast, no hallucinations, structured, confidence output, parallel, calibrated. Their BS is their own demise.
I think Laya's author genuinely bought their BS and thinks he built the same thing. Unlike Typesafe, I don't think he's intentionally misleading people.
The only unique thing about Jev is that it's a general purpose classifier. Funny enough, they were so busy spreading marketing bullshit that they forgot to mention the only real thing that makes Jev unique.
I suspect most people only read the blog post, and thought it was great how a VC company "stole" an idea and was "outdone" by a rando... without actually checking the facts. Confirmational reading bias, we live in a post-truth world with dysfunction media ecosystem
But like you said, at the end of the day he's just a rando.
He's not asking for $40m, not saying "I made ChatGPT, but i hate it, so I built the next big thing". Not claiming to co-invent RLHF.
Laya is just noise. Jev's bullshit affects me today - I see people injecting it into the codebases where it has no place.
Just how to get $500k ARR fast
https://news.ycombinator.com/item?id=49674396
if you use gateways, GoModel support the S1 endpoints, my favorite feature is the virtual models, stable name, I can swap out the backing model(s)
https://gomodel.enterpilot.io/docs/getting-started/quickstar...
(the "kev" in the docs is my fault, I should have said Jev / System1 in my feature request)
> It is an independent project, not affiliated with Ollama.
[1]: https://ollaya.dev/docs/faq
Smarter move if you have an eval set is to just train a classifier and call it a day.
top open one is trained by perplexity cto for $3k, kinda cool https://x.com/denisyarats/status/2102252088067850507
Bro is writing off the H200 lol
On a sidenote I really can't stand the term "swarm" and definately plays into AI doomerism.
(already merged)
GoModel (gateway) already supports Jev like endpoints too
https://gomodel.enterpilot.io/docs/providers/jev