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.
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?
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.
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.
Can play Doom too - https://x.com/vinnylarouge/status/2100281651930513460
https://xxcancel.com/vinnylarouge/status/2100281651930513460
That was super quick.
Insane
https://x.com/harshagundal/status/2100044305536889015?s=20
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.
Jev has a 32k context window. I doubt it's a large model.
I like it! I suspect Jev may have more going on under the hood, but I like the idea of efficient universal transformers
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.
All incorrect answers are marked with low-P.
It (DiffusionGemma with the Jev mode) can also solve an ASCII maze.
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?
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.
My guess would be qwen 2.5 predates linear attention which would be more complex to use.
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.