Reverse-engineered Jev-like model

Reverse-engineered Jev-like model(github.com)

115 points by rochansinha 12 hours ago | 16 comments

steeve 9 hours ago |

https://x.com/harshagundal/status/2100044305536889015?s=20

> They were building in stealth for 2 years, I was building in stealth for 2 hours…

> Happy to open source Qwen-2.5-1B-RLCD, 5x faster on-device inference for JSON workloads that need to be type-safe.

flockonus 9 hours ago | |

No question OSS is amazing, but this video is a satire at best. It doesn't take much attention to see the results on right vs. left side are significantly different.

Jev is not interesting if it's not "smart", a 1B param model is most definitely not smart.

nullbio 5 hours ago | | |

Jev has a 32k context window. I doubt it's a large model.

mmastrac 7 hours ago |

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.

razster 4 hours ago |

You can ask this Redditor saying he made it. https://old.reddit.com/r/LocalLLaMA/comments/1wihgum/i_liter... I think.

vrc 6 hours ago |

Out of curiosity and semi unrelated — why do so many of these projects with customized encoder-decoder setups use earlier Qwen versions like 2.5 and 3 and not the smallest 3.5? Purely the few 100m params, or something else in the latter’s arch or pretraining?

augment_me 6 hours ago | |

In my experience if you tell Claude to port LLM-like stuff without explicit steering for versioning, it will default to the most popular thing for this in its training window to reduce errors. 3.5 is outside its training data.

brainless 5 hours ago | |

I came across this recently. I was scanning for tiny models from HF using their search API. The script was generated by an agent. When I ran it, Qwen 3.5 did not make it at the top. Turns out, models generally prefer older content (training) but that the scanner also did not give any importance to recency.

FuckButtons 5 hours ago | |

My guess would be qwen 2.5 predates linear attention which would be more complex to use.

kylehotchkiss 2 hours ago | |

Anecdotally qwen2.5-14b runs a lot faster on my mini than the small 3 models ¯\_(ツ)_/¯

suresk 2 hours ago |

I've seen a lot of LLM uses that are really just zero/few-shot classifiers with a lot of extra steps, so it is interesting to see more models that are taking advantage of all the intelligence encoded in the latent spaces of these models with really efficient output. It feels like this is an under-explored area of LLMs right now and I'm excited to see what comes out of it.

rochansinha 12 hours ago |

Can play Doom too - https://x.com/vinnylarouge/status/2100281651930513460

DavCreator 11 hours ago | |

https://xxcancel.com/vinnylarouge/status/2100281651930513460

tomrod 8 hours ago |

I like it! I suspect Jev may have more going on under the hood, but I like the idea of efficient universal transformers

_superposition_ 10 hours ago |

That was super quick.

looksjjhg 8 hours ago | |

Insane