Archived. Von is no longer maintained and these weights will not be updated. For new projects, use LiquidAI/d1-omni-600M, a calibrated System One decision model that also handles images. The Von weights and von-sdk stay available for existing users.
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)
Calibration map, zero-shot Noul prior, and independent_options: true — the SDK reads this flag to run the order-invariant attention mode these weights were trained under. Do not drop it.
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.
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).
Cluster
Share
Sources and tasks
Operational & enterprise workflow
~25 %
IT ticket triage, customer intent routing (Banking77), billing/refund policy, warranty checks, order exceptions
Security, DevOps & compliance
~20 %
Secret-leak and SQL-injection screening, phishing, commit intent, on-call routing, PII
Safety, policy & moderation
~15 %
Ad policy, Fair Housing, travel-expense limits, ToS gating
Symptom and veterinary urgency, 311 routing, dietary/allergen checks
Adversarial reasoning anchor
~15 %
ANLI R1–3 + WANLI as Noul entailment and 3-way Choice, kept to anchor general logic
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).
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.
Chance-corrected competence (100 = perfect, 0 = chance) for Von 1.2 weights, per type and tier. Sealed items are never seen by any submitter.
type
half
easy
standard
judge
hard
Choice
open
91.0
37.6
36.7
17.0
Choice
sealed
50.2
30.7
7.2
11.3
Score
open
6.3
56.5
39.9
36.5
Score
sealed
45.0
29.6
29.6
28.0
Noul (1.2, raw posterior)
sealed
−92
−100
−96
−100
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)#
system
params
I
C
S
K
composite
easy
standard
hard
sealed
sealed ECE
Jev 1.13 (closed)
—
53.1
76.3
83.3
52.0
63.3
1.000
0.990
0.741
0.367
—
jeff (GLiFormer-large)
~400M
36.8
67.9
63.5
76.6
30.6
1.000
0.760
0.377
0.331
—
Laya (ModernBERT-large)
421M
36.1
63.7
71.1
86.2
30.3
0.944
0.729
0.341
0.308
0.172
Von 1.2
395M
34.5
75.7
70.5
77.8
27.5
0.931
0.688
0.373
0.279
0.107
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.
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.
@software{von2026,
title = {Von: An Open-Source System One Decision Model},
author = {Panisa, Victor Hugo},
year = {2026},
url = {https://github.com/wfzyx/von}
}
Files
12 files · 3.16 GBWeights 1.58 GB (safetensors). Sizes and sha256 from the signed manifest.
