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Infrastructure2026

Noma

Calibrated decisions in milliseconds, self-hosted

An open decision model for agents and pipelines. Give it a state and typed questions; get back calibrated probabilities, an abstain signal and an uncertainty measure. It never writes text.

//The problem

Using a chat model as a decision-maker means generating a sentence and then parsing it — slow, brittle, and with no calibrated confidence. There is also no clean way for it to say "none of these" rather than picking the least-bad option.

//What it does

  • A sliced decoder with trained classification heads instead of text generation.
  • A separate trained abstain head, rather than thresholding a confidence score after the fact.
  • Serves the same /v1/systemone API as Jev, so it drops into existing integrations.
  • Open weights under MPL-2.0 and a published PyPI package.

//Measured

Latency
~16 ms end-to-end on one GPU
Ablation
18 of 32 backbone layers score the same as all 32
Released
PyPI blackdrome-noma + Hugging Face weights
Paper
DOI 10.5281/zenodo.23186353

//Built with

  • PyTorch
  • Open weights
  • FastAPI
  • Calibration

status: Released · open weights