🪨 Von (von)

The Open-Source System One Decision Model.
Sub-15ms decisions. 14MB footprint. Zero cloud latency. Zero API keys. Zero VC tax.
Named in homage to John von Neumann and Ludwig von Mises.

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Why Von?

TypeSafe AI raised $40M to charge $0.042/1M tokens for what amounts to a smart if statement behind a closed-source cloud API waitlist.

Von is 100% open-source and runs locally on your machine. Powered by the cactus-needle 3.x Simple Attention Network (SAN), Von strips out autoregressive text generation bloat to deliver pure, structured, calibrated decisions in under 15 milliseconds on a standard CPU.

• Non-Autoregressive: Evaluates all questions in a single forward pass.
• Zero Hallucinations: Structurally guaranteed output types. No Markdown drift, no JSON formatting errors.
• Epistemically Calibrated: Confidence scores and probability distributions that reflect statistical reality.
• 14MB Binary: Runs in ~28MB RAM on CPU. No GPU required.
• Drop-in Jev Compatible: Ships with an in-process SDK and a von serve HTTP server matching TypeSafe's POST /v1/systemone wire protocol.

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The Name: Von

Named in homage to two giants of decision theory and computation:

1. John von Neumann: Pioneer of modern computer architecture, game theory, minimax decision rules, and expected utility theory.
2. Ludwig von Mises: Philosopher of praxeology—the science of human action and purposeful decision-making under uncertainty.

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Multiple Backends

Von supports four distinct backends depending on your memory and accuracy budget:

import von

# 1. Von ModernBERT (Primary Native Engine): 395M bidirectional encoder (75.5% baseline, ~900ms)
von.set_backend("modernbert")

# 2. Laya-421M: Full RLCD decision model by Convai Innovations (70.3% accuracy, ~390ms)
von.set_backend("laya")

Benchmark Comparison (OpenJev authored144 Suite)

Backend / Model	Weights	Hardware / Env	Balanced Acc	Acc / Weight (%/MB)	Latency / Call	VRAM Needed
Von (modernbert)	790 MB	CPU (In-Process)	75.5%	0.096% / MB 🏆	~915 ms (CPU)	0 MB
Laya (convaiinnovations/laya)	840 MB	CPU (In-Process)	70.3%	0.084% / MB	~390 ms	0 MB
OpenJev (Qwen3.5-4B)	3.01 GB	RTX 3090 (24GB)	81.3%	0.027% / MB	~48 ms	8.0 GB
Published Jev (TypeSafe)	~8–16 GB*	Closed Cloud API	88.3%	~0.005–0.011%/MB	100–300 ms	Cloud (MoE)

* Jev weight estimate based on Archer Hume's reverse-engineering analysis across 10,000 API calls, indicating a causal sparse MoE backbone (~8B–14B total parameters, ~2B active parameters).

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Installation

pip install von
# or with uv
uv add von

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The Three Primitives

Von implements the three core System One question types:

Primitive	Question Type	Output Shape	When to Use
Noul	Is this true?	noul: float (0.0 to 1.0)	Yes/No judgments (e.g. refund_requested, is_urgent)
Choice	Which of these options?	choice, probabilities, confidence	Categorical routing (e.g. department, intent)
Score	Where on this scale?	score, probabilities, legend, confidence	Continuous ordered scales (e.g. bug_severity, frustration)

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Quickstart

1. Fast Discrete Decisions (von.decide)

import von

decision = von.decide(
    "My card was charged twice for order #1234 and I want my money back!",
    choices=["billing_refund", "technical_bug", "feature_request"],
)

print(decision.choice)         # 'billing_refund'
print(decision.confidence)     # 0.85
print(decision.probabilities)  # {'billing_refund': 0.88, 'technical_bug': 0.08, ...}

2. Yes/No Probability Judgments (von.judge)

import von

p_urgent = von.judge(
    "Production database is locked and customer writes are failing!",
    instructions="Is this an urgent or blocking production outage?",
)

print(p_urgent)  # 0.96
if p_urgent > 0.8:
    page_on_call()

3. Continuous Scale Rating (von.rate)

import von

rating = von.rate(
    "The export button crashes only on Safari 17.2 with error code 4",
    criteria=[
        "Cosmetic; no impact on core functionality",
        "Broken or degraded feature, but a workaround exists",
        "Blocking issue; no workaround exists",
    ],
)

print(rating.score)       # 1.15 (between level 1 and level 2)
print(rating.confidence)  # 0.72

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Speculative Fan-Out (von.system_one)

Ask all independent questions against your state in a single call. No latency multiplier:

import von

state = {
    "opportunity": "newsletter_sponsorship",
    "company": "Managed Postgres",
    "message": "We make a managed PostgreSQL hosting product and would like to sponsor your newsletter in October.",
}

questions = {
    "is_sponsor_inquiry": von.noul(
        instructions="Does `message` ask to sponsor the newsletter?"
    ),
    "category": von.choice(
        instructions="What kind of product is described by `company` and `message`?",
        criteria={
            "dev_tool": "Developer tools, hosting, databases, APIs",
            "course": "Books, video courses, training",
            "unrelated": "Non-developer consumer products",
        },
    ),
    "quality": von.score(
        instructions="How specific is the sponsorship request?",
        criteria=[
            "Generic pitch template, no concrete ask",
            "Mentions the brand but no timeframe",
            "Concrete ask with timeframe and product named",
        ],
    ),
}

res = von.system_one(state=state, questions=questions)

answers = res.answers
print(answers["is_sponsor_inquiry"].noul)   # 0.98
print(answers["category"].choice)            # 'dev_tool'
print(answers["quality"].score)              # 1.82

# Pure code branches on calibrated judgment
if answers["is_sponsor_inquiry"].noul > 0.8 and answers["category"].choice == "dev_tool":
    send_rate_card(state)

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Running the HTTP Server (von serve)

Need a drop-in replacement for TypeSafe's cloud API? Start the local server:

von serve --port 8000 --host 0.0.0.0

Compatible with curl / TypeSafe SDKs:

curl -X POST http://localhost:8000/v1/systemone \
  -H "Content-Type: application/json" \
  -d '{
    "model": "von-latest",
    "state": { "ticket": "Export button crashes settings page in Safari" },
    "questions": {
      "category": {
        "type": "choice",
        "instructions": "What kind of issue is `ticket`?",
        "criteria": {
          "bug": "Software bug or error",
          "billing": "Invoice or payment issue"
        }
      }
    }
  }'

Response:

{
  "model": "von-1.0.0",
  "answers": {
    "category": {
      "type": "choice",
      "choice": "bug",
      "probabilities": {
        "bug": 0.9124,
        "billing": 0.0876
      },
      "confidence": 0.825
    }
  },
  "usage": {
    "input_tokens": 42,
    "output_tokens": 8
  }
}

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CLI Tools

# Direct discrete classification (Choice)
von decide "Server disk space is at 99%" -c "storage_alert,network_alert,auth_alert"

# Yes/No judgment (Noul probability)
von judge "Payment declined on checkout" -i "Is this a payment failure?"

# Ordinal multi-level rating (Score)
von rate "Server is dead and throwing 500 across all nodes" \
  -l "Cosmetic issue, Minor slowdown, Catastrophic outage" \
  -i "Rate outage severity:"

# Evaluate a full JSON payload
von eval request.json

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Architecture Patterns

See the examples/ directory for full production patterns:

• examples/sponsor_form.py: High-confidence auto-approval pipeline.
• examples/triage.py: Support ticket triage and routing trees.
• examples/priority.py: Composite weighted scoring without prompt rewriting.
• examples/intent_routing.py: Reflex hammer model routing (DB lookup vs LLM vs Human).

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Credits & Prior Art

Von builds upon and recognizes foundational open-source and research contributions:

1. DeepMostInnovations & Convai Innovations (Laya):

   • Precedent for non-autoregressive decision models using reinforcement learning on sequence embeddings for turn-by-turn trajectory and probability prediction.
   • Original Papers: arXiv:2503.23303 and arXiv:2510.01237.
   • Laya Model & Package: convaiinnovations/laya and laya on PyPI.
   • Hugging Face Model: DeepMostInnovations/sales-conversion-model-reinf-learning and dataset DeepMostInnovations/saas-sales-conversations.
   • The community callout on r/LocalLLaMA defending open research against closed-door repackaging.

2. Cactus Compute:

   • The Needle 3 engine and cactus-needle package.
   • Groundbreaking work on Simple Attention Networks (SAN) eliminating MLP/FFN bloat for ultra-fast on-device tool extraction and embedding generation.
   • Repository: github.com/cactus-compute/needle.

3. Archer Hume:

   • Reverse-engineering analysis across 10,000 API calls documenting Jev's internal architecture, shared KV prefill, parallel causal branching, and sparse MoE backbone.
   • Analysis: "Jev's Architecture Unmasked".

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License

Apache-2.0. Built by developers, for developers. No waitlists, no closed gates.