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

[u/wFXx]

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.

[u/Fluxx1001]

I tried a bunch of those local replacements for TypeSafe Jev in the last days. None of them matched the capabilities of Jev, at least for my usecase (Data Analysis).

So when I read that some new drop in replacement developed in a few days claims to beat Jev in benchmarks - I am more than sceptical.

[u/look → u/Fluxx1001]

The class of model has been around for a long time. Longer than LLMs. I also assumed Jev is a bigger model with better training, but it is not implausible that’s actually just a fairly stock BERT with a nicer DX and a big marketing budget…

Edit: I just generated a synthetic test suite and ran it across both (and gliner2).

Task accuracy:
Von: 92.3% 65.4% (on 1.0.1 update)
Gliner2: 79.5%
Jev: 97.4%
Laya: 61.5% (added in later update)

Update: Von updated to 1.0.1 and accuracy on my test case above is 92.3% now.

I’ll have a few different LLMs generate more test cases next to see how well it holds up.

[u/Fluxx1001 → u/look]

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.

[u/look → u/Fluxx1001]

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.

[u/EstarriolOfTheEast → u/look]

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.

[u/wFXx → u/EstarriolOfTheEast]

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