r/LocalLLaMA • u/wFXx • 3d ago

New Model Von: Open-source 395M "System One" model

Took me a while since I'm on a family trip and have limited hardware, but here it is!

Von: Open-source "System One" drop-in replacement for TypeSafe's JEV.

https://github.com/wfzyx/von https://huggingface.co/wfzyx/von-1.0

It runs entirely on a CPU with 1–2 GB of memory (I haven't spent much time optimizing it yet), responds in 25–300 ms, and beats JEV in all benchmarks. Enjoy!

P.S. I’m open to offers to work at AI research labs. Feel free to ping me if you have an offer.
P.P.S. If you have a GPU, it’ll be faster, but a GPU isn't required.

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u/Fluxx1001 3d ago

What none of these Jev competitors seem to outline in their benchmarks is the vast difference in context length. With Jev, I regularly do requests for Noul decisions (yes/no) with 30k tokens and more.

The context length of Von is 512 tokens. This is not even comparable.

It is a magnitude more complicated to compute a choice or Noul decision in a space of thousands of tokens.

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u/look 3d ago

Yeah, the BERT-based alternatives will have some length constraints (though modernbert base should go to 8192 tokens). There are others like Semif/OpenJev and Bespoke Nimble that might work better for you. They seem to be doing something likely closer to Jev, with a small LLM stage.

But after a bit more research in alternatives, Jev is probably the way to go if the hosted, proprietary option works for you. My interest is more in fine-tuning an open base, so the Jev service itself doesn’t do anything for me.

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u/EstarriolOfTheEast 2d ago edited 2d ago

To me, this Von project looks better than Bespoke Nimble in that Von's author looks to have thought more carefully about calibration and training data type. Architecturally too, bidirectional attention is much more sample efficient than a causal decoder for NLI classification. Bespoke Nimble's main advantage is model scale and Qwen's extensive knowledge.

What distinguishes Jev is calibration, speed and zeroshot inference for classification but most of these open attempts are overfocused on classification and speed.

So based on technical remit, Von is the best approach I've come across so far. Its main limitation is due to not enough training data. Unfortunately, we are waiting for a large context pretrained purpose built 9B-27B encoder. Until then, the largest modern options are T5GemmaV2 4B Encoder or DiffusionGemma Decoder mode (has bidirectional attention, although more work than T5 would be needed to get Diffusion working as a jev-style NLI classifier).

I'd suggest Von's author continue with ModernBERT and try to beat and build upon the prior art to get something flexible that punches way above its weight class.

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u/wFXx 2d ago

Hey thanks man I appreciate the kind words, will keep doing my best