Reverse-engineered Jev-like model

rochansinha

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

_superposition_

That was super quick.

steeve

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.

tomrod

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

mmastrac

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.

vrc

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?

razster

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

suresk

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.