Ollaya – Ollama for open-source, Jev-style decision models (ollaya.dev)

220 pointsby Ardakilic4 hours ago68 comments

solaire_oa 18 minutes ago

I installed it, I tried the examples, it works.... But forgive my lack of imagination... what is this useful for?

Like, their example is of classification for a support interface.... `refund_requested`. Pretty convenient bool given the example is about a refund- what if 99% of submissions don't ask about a refund? Also, is that user not a `churn_risk`? What could possibly qualify as a churn risk if not a user asking for a refund?

https://ollaya.dev/library/laya The examples suffer the same problem of why I'd prefer to use a string column vs an enum. Changing an enum means you need to update the db, using a string you can do whatever.

I'm not trying to be negative, I genuinely want to know about some practical examples (that don't require tons of backwards maintenance).

alex7o 2 hours ago

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?

avereveard 2 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.

Swizec 2 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.

janalsncm 19 minutes ago

I don’t think that’s it. I sincerely doubt most developers are doing side by side comparisons of calibration quality.

OpenAI has a section on their embeddings model api page for zero shot classification. Of course you can choose an open weights embedding too if you’d like.

https://developers.openai.com/cookbook/examples/zero-shot_cl...

I think Jev wins on marketing and convenience. Most SWEs don’t want to talk about embeddings, cosine similarity, or precision/recall tradeoffs. They want something which plausibly works and is easy to use.

kakugawa 13 minutes ago

Jev's value becomes more apparent when the task is a moving target. eg an auto-mode classifier.

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.

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 3 hours ago

What are the models? I am super curious in these as well

simcop2387 an hour 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.

adinb 14 minutes ago

It doesn’t to be a ton bigger, 16k and reliable 8k would be a godsend. (I run at 2k)

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

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.)

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.

iamflimflam1 3 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.

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

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

philipodonnell 2 hours ago

What the best way to see how a homegrown version compares?

Havoc an hour ago

Amazing - was looking for some benchmarks around this earlier

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

jasonjmcghee 20 minutes ago

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.

pradn an hour 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..

janalsncm 35 minutes ago

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.

totetsu 21 minutes ago

Are you saying laya copied from jev, and released in two weeks? If so I don’t thinks it’s quite as simple a story as that. https://xtxinversexty.com/layas-prior-art-claim-is-absurd/

nacs 42 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?

ranyume 3 hours ago

>Run decision models locally.

>example is a text classification task instead of a decision

cobanov 3 hours ago

Fair point, that example is basically classification. I'll change it to something that looks more like a real decision.

OgAstorga 3 hours ago

text classification is equivalente to decision. This is exactly the same thing Jev does.

abirch 3 hours ago

Jev does it more efficiently because it doesn't use an LLM https://typesafe.ai/blog/introducing-system-one-models-and-j...

rockinghigh 2 hours ago

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).

ricardobeat 3 hours ago

It is not. In a benchmark with actual decisions - navigation, traffic, waypoints - laya does only slightly better than a small classifier.

ranyume 3 hours ago

If it has four legs, a tail and barks why not call it a dog?

gchamonlive 3 hours ago

Because this specific dog only barks in structured text

seemaze 2 hours ago

This dog only barks when given biscuits

hbrn 3 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 an hour 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 an hour 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 an hour 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 23 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.

verdverm 19 minutes ago

> He's not asking for $40m

Just how to "make fkn $500k ARR fast?"

https://news.ycombinator.com/item?id=49674396

too much LI/Xitter influencer consumption

nickstinemates 43 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.

mococa 3 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 30 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)

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)

thih9 an hour ago

FAQ[1] says:

> It is an independent project, not affiliated with Ollama.

[1]: https://ollaya.dev/docs/faq

qurren an hour ago

Would be great if you supported CUDA 12; I don't feel like paying $15K to upgrade my GPU right now

verdverm 30 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.

oguzhankayan 41 minutes ago

Nice work! Making open models easier to run locally is valuable on its own. Keeping the API compatible with Jev is a thoughtful touch, too.

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.

rgbrgb 3 hours ago

there's this thing with a bunch of similar models https://huggingface.co/spaces/multimodalart/jev-decision-ind...

top open one is trained by perplexity cto for $3k, kinda cool https://x.com/denisyarats/status/2102252088067850507

physicallyIllfr 3 hours ago

<<<"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.

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.

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

accountrequired 3 hours ago

and that ollama is go-llama and not rust, so it's not really the ollama of anything

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.

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!

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

https://gomodel.enterpilot.io/docs/providers/jev

amar-laksh 2 hours ago

This inference engine is soooo much faster btw: https://github.com/tamnd/kime