What is the point of this, if it is p90 17 seconds? Might as well use an LLM. The beauty of Jev is that it is dirt cheap and insanely fast.
zihotki 9 hours ago
I would hold your horses to paint it as dirt cheap.. In my cases for spam detection Luna was 20% cheaper due to prompt caching, although not as fast.
atombender 8 hours ago
> hold your horses to paint it as dirt cheap
For a moment I thought this was going to be a metaphor — maybe an ancient Chinese proverb about how paint brushes are made from horsehair and how you can't hold the horse to paint before you've turned the hair into a brush.
idiotsecant 7 hours ago
Darmok and Jalad, at Tanagra
tyre 8 hours ago
What are the costs compared to an ML model?
nico 8 hours ago
For email you can use a classifier
One way: separately embed sender, recipients, subject, body - then use the embedding vectors as input to a logistic classifier
With that setup, I get 95% accuracy on email classification, training on 50-100 base examples. The model trains on CPU in under 1min, and it does inference in under 20ms (most of it is running the embeddings, so you can make it faster if you train your own embeddings model)
That code applies the embeddings + classifier setup on the Banking77 dataset. It gets 93-94% accuracy depending on the embeddings you use (SOTA for this is ~95%, with much bigger and slower models)
That model scales very well with quantities of requests.
janalsncm 3 hours ago
I can’t see your gist but spam classification is a textbook example of something you shouldn’t measure with accuracy. If 95% of your samples are not spam you can get 95% accuracy by always guessing not spam.
You should use precision (when your model says “spam” how often is it spam?), recall (how many of the spam emails did it catch), or f1 (balanced between those two).
nico 3 hours ago
That's a great point. My case is not for spam, the classes are more balanced, but you are correct that precision, recall and f1 would be better measures for some of these tasks
calebhwin 7 hours ago
How are you benefiting from prompt caching for simple classification?
zihotki 7 hours ago
There are two parts in the data you supply to Jev for classification - the prompt describing your classification and the data. The data can be quite small - a simple chat message. And prompt part could be considerable since you need to describe your rubrics well.
With Jev you each time pay for your prompt, you can't cache it.
sarkarghya 5 hours ago
I mean, it sounds like it's only ideal for cases with significant system prompt overhead. I don't think Jev was built to have a large well described prompt setup. To me its more like a happy go lucky small label classification tool with important decisions left to stronger agentic models or yk humans.
jedberg 6 hours ago
Are you getting better performance from an LLM than a Bayesian classifier?
HawtAds 5 hours ago
How many requests per second do you have for spam that you are reliably hitting the Luna cache?
simplisticelk 4 hours ago
Is that just because the Jev implementation is less mature? Couldn't it also implement prompt caching?
esafak 9 hours ago
Jev ought to offer a flex mode that uses their spare capacity for a discount.
amelius 6 hours ago
Next step: make it classify the next word.
TN1ck 8 hours ago
I just did a run with a benchmark I just used to test other models against. (It's about detecting irony in german soccer tweets). On my M5 Pro with 48GB it took over 30min to decide on just 100 tweets, the thinking definitely takes long.
It performed quite below Jev, but above other open decision models I tested (68 correct vs 79 correct for Jev - see [1]). I'm running it for the moderation benchmark as well, but that will probably take a few hours on my machine.
Update: Jeeves took about 2 hours to moderate 394 data points and performed really well. It’s not as good as Jev, but it’s super close! In general, it’s super cool that you can tune how strict you want content moderation to be with these models.
nicowaltz 4 hours ago
cool to see!
thm 9 hours ago
Ask Jeeves - Only took us 30 years to come full circle.
tmnstr85 9 hours ago
this was the comment i came here for
onaclov2000 9 hours ago
My bots are all named Jeeves lol. I have a CLI tool I use that connects up to a LLM I made and I call it Jeeves too ...so funny. I really didn't use Jeeves all that much I tended to use...I think it was called Web crawler pre-google era
aftbit 7 hours ago
I used Altavista
victordmor 8 hours ago
I met one of the founders once in Oakland. Amazing fella.
davedigerati 7 hours ago
lol was thinking the exact same, named some ML projects Jeeves along the way...
rsingel 6 hours ago
Too true. I worked there.
Ask Jeeves hired hundreds of cheap liberal arts majors to classify data, some users thought Jeeves was real, the stock spiked when big companies hired Jeeves to automate support thinking it was a silver bullet, and the whole thing collapsed when a better model came along, and it degenerated into ripping off rubes with bottom of the barrel ads.
NetOpWibby 6 hours ago
Damn, what a way to go.
kridsdale1 6 hours ago
Sounds like the story of OpenAI in 6 years
kkukshtel 6 hours ago
You found the joke!
itzikkatz 5 hours ago
Cool engineering, but 17s p90 latency kind of defeats the point of a Jev-class model, which is supposed to be fast and cheap. Losing 10 points on MMLU along the way doesn't help.
trencedamp an hour ago
Jev noob here. I'm seeing all this jev talk and I understand the difference between this and normal models, but what are some actual use cases for jev?
theanonymousone 7 hours ago
This reminds me of "on-premise cloud".
teravor 7 hours ago
you don't need to post-train anything for this.
just get an LLM to think and then force it to output a specific json with prefill post-think.
make sure to include good conditioning text in the prompt with examples of exactly what the output should be like. you don't want dissonance in the probabilities on the prefill.
betenoire 7 hours ago
A classifier is a subset of generative text, so I think responses like this miss the point. Jev is cheap enough and fast enough to sprinkle across your app in ways that LLM would be infuriatingly laggy and unnecessarily expensive, and it's never going to be injected to provide a sorting algorithm in python.
The point isn't that new type of problem has been unlocked, rather a new approach that can unlock new use cases.
teravor 7 hours ago
this is exactly why Jev doesn't have thinking.
when you want a machine to reason about the prompt and generate a structured output not using an actual LLM makes no sense. I have been doing it since the first chain of thought open models became available.
perhaps there may be a way to get a Jev-type model to think for a very specific number of steps to gain control over its latency, if so that would be the next step. truncating LLM thinking like this does not work well, and its thinking isn't efficient anyway.
alienbaby 10 hours ago
Just curious, where has this term 'noul' come from for yes/no ansers?
/a bit more digging and..
A Noul performs a Bernoulli trial—an experiment with exactly two outcomes (yes or no)—but instead of picking one, it returns the calibrated probability (ranging from 0.0 to 1.0) that the statement is true.
I hate it :)
keepitwiel 10 hours ago
Bernoulli
user3939382 10 hours ago
If you want to get super pedantic about what’s happening in a transistor every digital Boolean is actually this
kevindamm 10 hours ago
Not quite.. that boolean is about whether the voltage exceeds some threshold. It's not about how close the voltage is to the circuit's maximum possible threshold, or how much it exceeds the threshold.
In an analog circuit, maybe.
k__ 10 hours ago
The whole "no hallucinations" premise is based on that.
Like, yeah, you don't hallucinate, but only because you force the user to decide in the end.
rusk 9 hours ago
Wait til you hear about how digital circuits work at die level
doginasuit 9 hours ago
That seems like the only possible way to eliminate hallucination, short of a model that is never wrong.
kjs3 9 hours ago
force the user to decide in the end
And that's...bad?
k__ 8 hours ago
Not entirely.
I think, it's a bit much to call this "no hallucinations".
Technically true, but in practice you could still choose the wrong result or the probabilities can be off.
doginasuit 9 hours ago
I like it. It is short and distinct which is a good fit for a primitive. It describes its fundamental meaning and draws a connotation with Boolean.
LudwigNagasena 9 hours ago
In Bayesian statistics that’s called credence. Weird that they felt the need to invent a new term.
finding_alfred 3 hours ago
Jeeves pretended to understand. Is there data showing Jev's actually calibrated?
winddude 5 hours ago
That completely defeats the point of something to make decisions faster.
swader999 10 hours ago
Seems like this is the way, a hybrid approach where some of the pipeline will be jev like and some traditional LLM depending on the nature of the work.
zerop 10 hours ago
Are there "good" Open source Decision models built on Gemma-4 and also trainiable on own data?
The number of people who feel the need to try and argue that you don't need Jev can only be astroturfing by those with something to lose - Anthropic and OpenAI employees.
Like it or not, companies are going to use Jev unless you can offer something just as cheap and fast.
I wonder just how much of the business automation market, previously held by LLMs, is at risk here?
swingboy 7 hours ago
Any good classifiers like this or Jev that support image input?
quantized_state 7 hours ago
I'd assume this would work with Qwen's image encoder probably better after a bit of tuning
How's the performance compared to ordinary 9B LLM with structured outputs? Both accuracy and speed?
RamblingCTO 9 hours ago
Super dope. If it would ship as prod ready code supporting mps as well that would be even doper.
But funny that jev is getting its lunch eaten apparently in under two weeks?
pavlov 9 hours ago
It’s ok, one week of AI hype is now enough to close a billion-dollar term sheet with VCs.
danieltanfh95 8 hours ago
it just a classifier. I guess we have to thank typesafe for spending VC money on marketing classifiers as decision models instead.
santadays 7 hours ago
Doesn't the fact that it's general purpose warrant a new term? It's partly that it doesn't need to be trained, but it's also able to play games based on game state, I'd imagine it would be hard to train a classifier to do something like this because you'd need to represent a good distribution of all the states. The general purpose llm world understanding underneath it allows for this.
I've used it to do web research where it follows the most appropriate links, decides what to record in state, etc. I struggle to see how you could implement something with a classifier. That said, I have no idea how deep the technology is and it might be replaced with open source pretty quickly since its drafting of the frontier models and the open source models seem almost as good.
I like the term decision model and I think it's warranted.
nico 6 hours ago
> I'd imagine it would be hard to train a classifier to do something like this because you'd need to represent a good distribution of all the states
Yes, one general classifier would be very hard to train. However, you can create a sort of ensemble of classifiers, each trained in different tasks
I’m currently experimenting with this. So far I’ve combined classifiers for 13 different datasets, my target is 95 (the ones Laya used for training)
svachalek 5 hours ago
This isn't eating Jev's lunch. This is someone who doesn't understand the entire use case of Jev replacing it with something that doesn't handle it at all.
woadwarrior01 10 hours ago
This isn't really surprising. LLM reasoning and before that, chain of thought prompting are essentially forms of test-time compute scaling.
loclol101 8 hours ago
How general really are these jev type models? Has anyone done any broad very cross-domain eval on them?
Naitik88 9 hours ago
what about benchmark against smaller or bigger models? 9B looks too small for llm-level decisions.
AnodicElegy 9 hours ago
I'm surprised we haven't seen a "Jehovah" yet.
jadar 9 hours ago
With the amount of talk about "inventing god", I'm surprised too.
mxkuzn 9 hours ago
interesting bench list, what about benchmark against smaller or bigger models? 9B looks too huge for small like laya, and too small for llm-level decisions.
