von-ONNX

von (ONNX)#

This is an ONNX version of wfzyx/von. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js#

See the pipeline documentation for zero-shot-classification: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ZeroShotClassificationPipeline

Von 1.3#

Von is an open-source, non-autoregressive System One decision model: it picks an option, judges a condition or rates a level in one bidirectional forward pass, with no token generation. This card is about the weights. Install, CLI, wire protocol and examples are in the repo: https://github.com/wfzyx/von

• Version: 1.3 (von-1.3.0; engine release, weights unchanged from 1.2)
• Size: 395M (ModernBERT-large backbone + Option-Marker scoring head), 1.5 GB fp32
• Context: 8192 tokens
• License: Apache-2.0
• Weights SHA: 5df8185a4f2327ad0a7cd117cc4f701ac557b9ae

pip install "von-sdk>=1.3.7"

Previously published as wfzyx/von-1.0; the old id redirects.

Files#

Architecture#

The state, the question and every candidate option are packed into one sequence. Each option gets a [MASK] marker whose final hidden state is scored by a small head; a softmax over the marker logits is the answer distribution. Cost is one encoder pass regardless of option count. Noul is a two-option Choice over "condition holds / is false" descriptions (or the caller's criteria); Score is a Choice over level descriptions with the expectation taken over the distribution.

Order-invariant scoring (since 1.2). Inside the encoder an option's tokens attend only to the shared premise and to that option's own tokens, and every option's position ids restart at the end of the premise; ModernBERT's sliding-window attention is computed from those ids. Each option's logit is therefore a function of (premise, that option) alone: permuting the options permutes the logits and changes nothing else. Measured on JevBench hard, 111 items × 4 orderings: 1.1 flipped 49.5 % of answers, 1.2 flips 0.

Training data#

Base: ModernBERT-large (2T tokens general web, technical text and code). Fine-tuning is a listwise softmax cross-entropy + Brier objective over the option markers, with independent-options masking and digit-split tokenisation, on a ~290k-item balanced decision corpus, then continue-trained on synthetic two-hop and numeric sets. Every row is the same shape the model serves: state, question, described options, gold (or a soft target distribution where the source gives one).

No JevBench item, public or sealed, is used for gradient training. The calibration map was fitted on the 231 public JevBench items (see Calibration); jabr v2, Decision Index and the coding-agent probes below are fully out of sample.

Corpus builders are in the repo under training/ (prepare_universal_dataset.py, prepare_decision_dataset.py, generate_synthetic_decisions.py, prepare_judge_diversity.py for the judge-shaped and label-diverse public mix used in 1.4 experiments).

Calibration#

Confidence comes from an input-conditioned temperature stored in marker_calibration.json: T = bias + w_H·H_norm + w_len·log10(state_tokens)/4 + w_K·(K/8), clamped to [0.3, 12], where H_norm is the normalised entropy of the raw option distribution. Temperature is monotonic, so it never changes an answer, only how sure the answer claims to be. Noul additionally passes through the band decision rule (p' = 0.8 + 0.1·(p−0.5), mirrored below 0.5) so every yes/no commits outside the 0.2–0.8 abstention band; --noul-decision raw returns the calibrated posterior.

The shipped map is in-sample on JevBench public (Calibration axis 77.4 in-sample, 67.6 split-half) and does not carry to other distributions — out of domain Von can be confidently wrong. von calibrate labels.jsonl refits it on your labels with frozen weights on CPU in minutes; on a 38-label coding-agent probe set a scalar T = 2.15 took ECE from 0.21 to 0.11.

Benchmarks#

JevBench v1.5.1 (live board, sealed + open halves)#

Image: sealed-half competence by tier, ≤600M encoders

Chance-corrected competence (100 = perfect, 0 = chance) for Von 1.2 weights, per type and tier. Sealed items are never seen by any submitter.

Calibration 83.5, Cost 82.7, median latency 0.92 s (board measurement). The Noul row is the v1.5 abstention rule meeting 1.2's hedged posteriors: nearly every Noul answer fell in the 0.2–0.8 band and was scored as an abstention, which zeroes the Intelligence axis for this row and for most calibrated encoders on the board. The band rule in 1.3.3+ fixes the decision without touching the weights (public Noul competence −76.5 → +18.9 on JevBench, −42 → +32 on jabr v2); the board row awaits re-measurement (JevBench #143).

Sealed Choice ties the 151M Verdict 1.4 (17.9) and trails the 194M GLiNER2 (21.0); Von leads the encoder class on sealed Score (30.5) and open Choice (34.4). Von's open→sealed Choice gap (14.7 pp against a field median of 5.2) is the clearest thing on this board and is what 1.4 is aimed at.

JevBench v1.4 (last board with a Von Intelligence score)#

Speed 70.5 is an unmeasured carry-over; remeasured under the JevBench protocol Von runs 0.096 s raw p50 on 4 vCPU (OpenVINO, Speed 89.0) and 0.023 s on an A10G (94.2), inside the Jev-class latency line. With the 1.3 chain-of-options engine on the same weights, public hard goes 0.378 → 0.441 locally (2 vs 9 discordant, McNemar p = 0.065) with zero discordant pairs on easy, standard and jabr v2.

Out of sample#

Every accuracy claim goes through a paired exact McNemar test with a minimum detectable effect; results below the MDE are reported as UNRESOLVABLE.

Limitations#

• English only. Other languages get token overlap with unearned confidence.
• Selects and scores; does not write text.
• Weak on long multi-clause policy and multi-hop temporal/numeric composition (hard tier ~0.37–0.44); the chains engine covers the computable subset only.
• judge without criteria is the weakest path: generic "holds / is false" descriptions plus surface cues. Pass criteria or phrase as a described Choice.
• Known training gap: auth failures (401/403), rate limits and tool rejections route to a generic code-error option with high confidence (issue #22).
• Calibration map is JevBench-shaped; refit with von calibrate if you act on the confidence values.

Citation#

@software{von2026,
  title  = {Von: An Open-Source System One Decision Model},
  author = {Panisa, Victor Hugo},
  year   = {2026},
  url    = {https://github.com/wfzyx/von}
}