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Introducing System One Models and Jev

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Re: Introducing System One Models and Jev

#351

Post by xixixao »

lubujackson wrote: post #339
wg0 wrote: post #327

Earlier quoted context omitted.

Curious what your use case is if not confidential.

Not confidential, but not super relevant, as this is something I have learned the hard way over the past year across various projects. A lot of people have become prompt maximalists, asking for complex multi-part solutions or dynamic workflows in a single prompt. You can get this to work sort of reliably with frontier models, but without much confidence or clarity where things might break in practice. My goal is to s…

Example I have worked: Personal delivery app, that tracks packages from various senders using incoming emails.

I am using the single prompt approach with GPT5.4, which is free, but it’s not reliable. Using Jev I’d decompose the prompt into a bunch of smaller questions, then I’d combine the answers in software. I’m super excited to try Jev out.

Re: Introducing System One Models and Jev

#353

Post by padolsey »

I'd love to know if Jev is still fundamentally LLM-shaped in architecture. Like is it using a single forward pass with a learned readout over the predefined options (i.e. a discriminative head on a transformer, no decoding), or something else? I did similar things for zero-shot criterion-based classification using a 4B Qwen model but could not reach the level of intelligence they've got here. Tho speed/cheapness was similar.

Re: Introducing System One Models and Jev

#354

Post by nullbio »

latteren wrote: post #341

Looking at the example Jev use cases, it almost feels like Jev's incredible cost/task can make it competitive as a generalized "poor man's ranking" algorithm that can be useful for lean startups or any fast paced development org. I need to rank 1000 articles and pick the 5 most relevant for the user? Jev. I need to audit and strip out content because my user is affected by regional privacy laws (without hallucinating…

More like:

I need to ...? -> Open-weight model.

I'm sure someones working on this as we speak using an open-weight LLM base (Qwen or something would be a perfect fit).

This sort of task is a perfect fit for a very small model capable of semantic parsing. You can get away with a LOT less parameters without all the autoregressive generation and long-context reasoning.

Re: Introducing System One Models and Jev

#355

Post by ramoz »

VladVladikoff wrote: post #308
ramoz wrote: post #306

Earlier quoted context omitted.

GLiClass is performant, and its zero-shot classification scores are in the same ballpark as the Terra-level results Jev points to. https://github.com/knowledgator/gliclass

Thanks!

Rereading some things and because there's no official benchmarks, I misspoke about the ballpark comparison., but the open model's still a useful foundation to work with

Re: Introducing System One Models and Jev

#356

Post by [deleted] »

AdieuToLogic wrote: post #338
futurisold wrote: post #172

This, combined with contracts, could make a lot of things so much fun now! For those who don't know (which is probably everyone but me), I ported the design-by-contract pattern in Python and combined it with LLMs. This was early 2025. I originally wrote about it here: https://leoveanu.com/2025-03-01-dbc/ . Contracts are a core feature of SymbolicAI ever since. The community seems to have loved it too ( https://news.y…

[flagged]

[deleted]

Re: Introducing System One Models and Jev

#357

Post by [deleted] »

futurisold wrote: post #172

This, combined with contracts, could make a lot of things so much fun now! For those who don't know (which is probably everyone but me), I ported the design-by-contract pattern in Python and combined it with LLMs. This was early 2025. I originally wrote about it here: https://leoveanu.com/2025-03-01-dbc/ . Contracts are a core feature of SymbolicAI ever since. The community seems to have loved it too ( https://news.y…

[deleted]

Re: Introducing System One Models and Jev

#358

Post by soleveloper »

jacobgold wrote: post #81

First, congrats to the team on launching something genuinely interesting and new. Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that. This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do. Jev can only generate structured out…

I think the meaning of can't hallucinate in this model is that the type won't be hallucinated.

So if the generated schema is for a tool call for calculator, then the numbers will be valid numbers for sure (and not random words).

To me, it looks similar to BNF schema already introduced and implemented few years ago: generally speaking - it limits the next token that is allowed to be generated, probs are drawn from a subset tokens.

(tbh, I'm not sure why it didn't pick up as a more standard interface to LLMs, as it made a lot of sense back then, and now.)

Re: Introducing System One Models and Jev

#360

Post by techn00 »

bregmandiv wrote: post #186

I'm trying to parse it down to what we had before vs what is new here. We already had encoder models that skipped text generation for giving us a numerical output that could be computed as a probability. we also got no hallucinations and faster inference for free there. So we already had 1. "unstructured state in, probabilistic decisions out" 2. "orders of magnitude faster and more efficient" What was hard there was…

I can't see how this is different from a fine tuned LFM2.5 encoder
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