vllm

Jev is just a trained model afawk., the (incomplete) papers the Laya author uses to claim they had the idea first describe RAG systems, orchestrators, and related built on LLM APIs. Laya came out after Jev and appears to be vibe coded to look more like Jev, then the claim of "idea theft" was made with the new blog post and code base, pointing back to papers that describe nothing like Jev. Stepping back from Laya, there are dozens of Jev-a-likes now, most built on LLMs, whereas it is speculated that Jev likely uses transformers, but is not LLM like by generating a token at a time. Most likely they are reaching into the logits to get the details and assembling the json object deterministically. This is what the various LLM spins are doing, which is probably close but not the same as Typesafe. This is the main PR (merged) for using Diffusion

In the olden days we call this classifier, usually assignment 2 of Machine Learning 101. BERT (well, GLiNER specifically) and diffusion are calling and want their Large Classifier Models back. https://github.com/vllm-project/vllm/pull/57250

With all the excitement around Jev, I suspect we'll see hundreds of options, it doesn't sound like Jev is that hard to replicate, given all the 3rd parties who are getting pretty damn close, or even better, results within a week. vLLM has a PR very close to merging: https://github.com/vllm-project/vllm/pull/57250 Kev is an open Jev: https://github.com/jaredpalmer/kev

related, vLLM PR nearing merge readiness for Jev like capabilities with diffusion models https://github.com/vllm-project/vllm/pull/57250

I have a quirky vLLM on k8s on 2x OEM sparks setup with 9 models available to me. I'm not keen to run nightly vLLM, too many issues with it in the past. Going the qwen-next path means displacing things I use daily :/ I have a watchful eye on the diffusion ~ Jev/Kev PR https://github.com/vllm-project/vllm/pull/57250

paper the reddit OP "published" (their words on reddit) to arxiv (before they put the vouching process in place). It's what you expect if you click through. https://arxiv.org/pdf/2503.23303 Does not appear to be like what Jev is doing, they talk about RAG and embeddings and orchestrators (the stuff that was cool 1 year ago), no talk of system 1 vs 2 (before Jev), whereas Jev is apparently just a model. There is a vLLM PR introducing Jev like capabilities for diffusion models (and more, have not delved deeply) https://github.com/vllm-project/vllm/pull/57250

If you want to try a _legit_ Jev implementation that matches (at least in my evals), the vLLM patch to turn DiffusionGemma into Jev is available. On my DGX Spark I get very similar latency numbers, and it matches my evals + or - a few points on each test (DG wins some, Jev wins some, both show low confidence when wrong). I ran the same evals against a Qwen36 and it clearly lost to both of them, so you are leaving both knowledge and instinctual reasoning on the table with any smaller models, FWIW. https://github.com/vllm-project/vllm/pull/57250

There is an open PR for VLLM to do this via DefussionGemma https://github.com/vllm-project/vllm/pull/57250

yup https://github.com/vllm-project/vllm/pull/57250

Sure, just wait for this PR to land : https://github.com/vllm-project/vllm/pull/57250

Any diffusion model is potentially a Jev in disguise: https://github.com/vllm-project/vllm/pull/57250 Runs ~0.2s per decision on my DGX Spark. 10/10 programming language detection 9/10 human language detection 10/12 unit magnitude comparison All incorrect answers are marked with low-P. It (DiffusionGemma with the Jev mode) can also solve an ASCII maze.

I was hoping to see the reasoning_content mess get robustly fixed, but all we got was this doc change: https://github.com/vllm-project/vllm/pull/50624