gnumanth/nullspan (v1.3.1)

nullspan v1.3.1 ships two zero-output-token System One neural models in one open-weights MIT repository:

1. NullspanForExtraction (345.3M PyTorch FP32 + 331.1 MB Int8 ONNX, 512-token context + long-document windowing): Grounded [start, end] span extraction, calibrated p_null abstention (with dedicated qa_null_head), multi-numeric contract disambiguation (96/96), cross-field algebraic constraint verification, and (p10, median, p90) quantile estimation.
2. NullspanSystemOneEngine (395M 8K ModernBERT-large with Exact Tree-Isolated Parallel-Option Masking + Cross-Question Workflow Competition + 24 Latent Factor Heads, systemone/): Native /v1/systemone calibrated decision model for noul (true/false), choice (options), and ordinal score (min..max) rubrics in a single forward pass per case (47.8 ms/case, 9.56 ms/decision on L4) with 0 generated output tokens and 0.0 cross-option RoPE bleed (100% option-order permutation invariant).

Verified Public Benchmark Scores (Snapshot 2026-11-11 · Zero Test Contamination)

Training Disclosure (mode: specialist): NullspanSystemOneEngine (v1.3.0 weights pinned at HF commit 51acf9e097ade3fb1068b3511490ef2756485927) is trained via Stage 1 general System One pretraining (BoolQ, ARC-Challenge, SciQ train splits + programmatic numeric/policy/negation/adequacy rubrics) followed by Stage 2 10-model 5-fold Cross-Backbone OOF Self-Distillation + Trajectory-SWA on the LocalLLaMA/typed-decisions train split; it is not zero-shot on Typed Decisions. All test/evaluation splits (Typed Decisions test, JevBench public & sealed suites, SQuAD v2.0 validation, and NullspanBench evaluation pools) were strictly untouched during training and OOF calibration. Prior v1.2.1 weights remain immutably preserved at commit 43cd3a296bbc858189d1e183eaed6e89f95e5dfd.

Quick Start — Grounded Extraction (NullspanForExtraction)

from transformers import AutoModel, AutoTokenizer

repo_id = "gnumanth/nullspan"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)

incident = (
    "Incident INC-40928 opened on Apr 12, 2026 with a 502 Bad Gateway errors and a TLS handshake timeouts. "
    "Issued $1240.50 plus $310.25 SLA credit. Severity is sev-1 and customer data loss was false. "
    "Root cause CVE not yet assigned. Cluster stabilized on Apr 19, 2026. Reported by Mira"
)

result = model.extract(
    state=incident,
    fields={
        "incident_id": {"type": "text", "instructions": "Incident ticket identifier"},
        "opened_on": {"type": "date", "instructions": "Date the incident opened"},
        "total_credit": {"type": "number", "decimals": 2, "unit": "USD", "instructions": "Total credit including SLA penalty"},
        "outage_days": {"type": "duration", "unit": "days", "integer": True, "instructions": "Days from incident open to cluster stabilization"},
        "failure_modes": {"type": "list", "items": "text", "instructions": "Each failure mode triggered"},
        "severity": {"type": "enum", "options": ["sev-1", "sev-2", "sev-3"], "instructions": "Incident severity tier"},
        "data_loss": {"type": "boolean", "instructions": "Whether customer data loss occurred"},
        "cve_id": {"type": "text", "instructions": "Assigned CVE identifier if present"},
        "remediation_cost": {"type": "estimate", "unit": "USD", "min": 100, "max": 25000, "scale": "log", "step": 10, "instructions": "Estimated engineering remediation cost"},
    },
    tokenizer=tokenizer,
)
print(result["answers"])

Quick Start — /v1/systemone Typed Decisions (NullspanSystemOneEngine)

from nullspan import decide

res = decide(
    state={"customer_tier": "enterprise", "RefundCapUSD": 500, "RequestedRefundUSD": 420, "FraudFlag": False},
    questions={
        "eligible": {"type": "noul", "instructions": "Is RequestedRefundUSD <= RefundCapUSD with FraudFlag == False?"},
        "route": {"type": "choice", "options": ["approve", "escalate", "deny"], "instructions": "Select refund action."},
        "urgency": {"type": "score", "min": 1, "max": 5, "instructions": "Rate SLA urgency from 1 to 5."},
    },
    model_id="gnumanth/nullspan",
    revision="51acf9e097ade3fb1068b3511490ef2756485927",
)
print(res["answers"])

Or serve the TypeSafe-compatible /v1/systemone HTTP endpoint directly:

pip install "nullspan[hf]==1.3.1" fastapi uvicorn
python serve_systemone.py --model-dir gnumanth/nullspan --port 8010

Repository Contents

• model.safetensors, config.json, modeling_nullspan.py: NullspanForExtraction (345.3M ELECTRA-Large + 2-layer Pre-LN SchemaAdapterBlock + qa_null_head).
• onnx/model_quantized.onnx, onnx/tokenizer.json: 331.1 MB dynamic QUInt8 ONNX model powering offline Browser WASM (https://hemanth.github.io/nullspan/) and CPU inference.
• systemone/, systemone_decision.pt, systemone_decision.py, serve_systemone.py, predictions_typed_decisions_test.jsonl: NullspanSystemOneEngine (395M 8K ModernBERT-large with Exact Tree-Isolated Parallel-Option Masking, ParallelSpanDecisionHead, Cross-Question Workflow Competition, and 24 Latent Factor Heads) and /v1/systemone FastAPI server.
• .eval_results/typed-decisions.yaml, .eval_results/nullspan-bench.yaml, SCIENCE_REPORT.md: Reproducible benchmark metadata and statistical report.

License

MIT © Hemanth.HM