Statim Decide EN Large

Licence notice (2026-10-03). Version 0.5.0 of this model was trained partly on data whose texts carry no known licence (SDOH-NLI, LEDGAR). From 2026-10-03 these weights are offered only under PolyForm Noncommercial 1.0.0; the Small Business and Free Trial licences and the commercial licence do not apply to them. A replacement trained only on cleared data will follow. Details: DATA_LICENSES.md.

A decision model for Statim, the native C++ engine for typed decisions: ask any text a choice, a score or a yes/no question and get calibrated answers from one forward pass, on CPU or GPU, without Python at runtime. Version 0.5.0, fine-tuned from convaiinnovations/laya (ModernBERT-large encoder).

▶ Try it live in your browser: the Space runs the multilingual model on a free CPU; this English model is served the same way.

One support ticket, three typed answers, one forward pass: the 60-second film.

Quick start

# Statim release binary: https://github.com/BEKO2210/statim/releases
huggingface-cli download Beko2210/statim-decide-en-large statim-decide-en-large-q8_0.gguf --local-dir models
./statim serve -m english=models/statim-decide-en-large-q8_0.gguf --port 8080
curl -s localhost:8080/v1/systemone -d '{
  "state": {"subject": "Duplicate charge on invoice #4411",
            "body": "We were billed twice for March. Please refund the second charge."},
  "questions": {
    "department": {"type": "choice", "instructions": "Which department should handle this?",
      "criteria": {"billing": "invoices, refunds", "technical": "bugs, outages", "sales": "pricing, contracts"}},
    "urgency": {"type": "score", "instructions": "How urgent is this request?",
      "criteria": ["not urgent", "soon", "critical"]},
    "refund": {"type": "noul", "instructions": "Does the user explicitly request a refund?"}}}'

Open http://127.0.0.1:8080/ for the playground. API reference: docs/API.md.

Files

Checksums in SHA256SUMS. The f32 file reproduces the reference implementation within 1e-4 on Statim's parity tests; q8_0 is smaller and faster on CPU with slightly different logits.

Evaluation

Measured by Statim's no-harm gate (tools/finetune/gate.py) on held-out test data the model selection never looked at. Against the checkpoint it was trained from, on 54 held-out suites: 11 significant gains, 43 within noise, 0 regressions (two combined binomial standard errors).

Published systems under the same protocol, for orientation: typed-decisions meraGPT 0.768, laya-typed-decisions 0.766, Jev 0.727; AG News zero-shot Laya 0.950, GPT-3 (CARP) 0.926, Jev 0.881; Banking77 supervised MPNet 0.941. Sources: docs/ROADMAP.md.

Reproduce these numbers: REPRODUCE.md.

Training

Multi-task fine-tuning with train_multitask.py --clean from checkpoint laya, best epoch 11/raw selected on validation data only. Training data: only sources whose licence permits commercial use and imposes no ShareAlike or copyleft terms (Banking77, MASSIVE, typed-decisions replay, a licence-audited tasksource mixture, Nemotron-Safety-Guard, IndicGuard, MINDS-14, SNIPS), every one listed with its licence in DATA_LICENSES.md. Evaluation test rows were removed from the training data.

Intended use and limits

• Classification-style decisions over short texts and JSON: routing, triage, moderation, intent, yes/no checks, ordinal ratings. It does not generate text.
• Accuracy varies by task and language (see the table). Reading comprehension (Belebele) and semantic similarity are weak; do not use it for them without your own evaluation.
• Use the confidence: Statim's min_confidence option marks low-confidence answers with escalate: true so a person can review them. Do not automate high-stakes decisions about people without human review.

Licence

The weights may be used under any one of: PolyForm Noncommercial 1.0.0, PolyForm Small Business 1.0.0 (free commercial use below 100 people and 1 M USD revenue), PolyForm Free Trial 1.0.0 (any company, fewer than 32 days), or a Statim commercial licence (COMMERCIAL.md). Texts in LICENSE-MODEL.md. The Statim engine is Apache-2.0.

Built on Laya (Apache-2.0) and ModernBERT-large (Apache-2.0). Training data attribution: Banking77 (Casanueva et al., 2020, PolyAI), MASSIVE (FitzGerald et al., 2022, Amazon), and the CC-BY sources in DATA_LICENSES.md. Statim is independent and not affiliated with the Laya authors.