Decima-base: an open, calibrated Jev-style decision model
Give it a situation, a question and your options. Get back calibrated probabilities.
Decima is a System One model: it returns typed decisions instead of generating text, and speaks TypeSafe's
/v1/systemone wire format. The decision types are choose one, yes/no, ordered score and rank.
Decima-base is the second generation and the larger sibling of decima-small:
- 321M parameters on mmBERT-base;
- int8 ONNX on a CPU;
- the same answer whatever order the options come in.
For coding-agent hooks (secret gate, bash gate, tool and command choice), use decima-agent, fine-tuned from this model.
Built by A. M. Madani (@amyrmahdy). The name: DECIsion MAking, and Decima is also the Roman Fate who decides.
| decima-small 1.1 | decima-base | Laya-multilingual (same backbone, same size) | |
|---|---|---|---|
| Parameters | 122M | 321M | 322M |
| jabr/classifier-benchmark v2, 49 tasks (macro accuracy)¹ | 0.616 | 0.673 | — |
| The same 49 tasks in Persian (our Persian edition)¹ | — | 0.616 | — |
| Laya's MASSIVE + XNLI protocol, re-run by us, 29 suites / 19 languages² | 0.761 | 0.793 | 0.607 |
| Kev's published protocol, re-run by us, 8 suites | 0.762 | 0.790 | 0.580 |
| Decima bench, 12 suites, EN/FA/AR/RU² | 0.786 | 0.794 | 0.554 |
| BTZSC, 18 zero-shot suites (macro-F1)³ | 0.558 | 0.594 | — |
| Calibration error as shipped (ECE, mean over 48 suites) | 0.055 | 0.051 | 0.250 |
| Answer changes when options are shuffled | 0 % | 0 % | 21 % |
¹ int8 on CPU through the benchmark's own harness. On jabr v2, the benchmark's published runs: Jev 0.966, Von 0.720, GLiNER2 0.684, Laya 0.583. ² In-distribution for Decima: it trained on the MASSIVE and XNLI/MNLI train splits (test rows differ). Laya reports it did not. ³ BTZSC suites are zero-shot for Decima: none of their training data was used.
It also plays games. These are sensor text in, one decision per move out, with the games' wording never in training:
- it wins 9 of 10 levels of a side-scrolling platformer;
- it averages 22.6 apples per game of Nokia-style Snake.
Quickstart
pip install decima-ai # the `decima` package, CLI and server (PyPI; source: github.com/amyrmahdy/decima)
from decima import Decima, Question
m = Decima.from_pretrained("amyrmahdy/decima-base") # int8 ONNX, CPU
q = Question("Which team should handle this?", ["billing", "technical support", "sales", "account access"])
d = m.decide("I was charged twice for the same subscription this month.", q)
print(d.top, d.probs)
From the command line, with ready-made presets (triage, routing, moderation, pii, guardrails, …):
decima "Our checkout is down and customers are leaving" --preset triage
As a local TypeSafe-compatible server:
python -m decima.serve --model amyrmahdy/decima-base --port 11436 # binds to 127.0.0.1
The official typesafe-sdk works against it unchanged.
How it works
The architecture is a late-interaction scorer:
- the state and question are encoded once;
- each option is encoded separately and cross-attends to the state;
- there is one logit per option.
Options never see each other, so permuting them permutes the probabilities exactly (0 % flips by construction).
score uses an ordinal head, and one fitted temperature calibrates every output.
The state budget is 512 tokens, and a longer state is rejected (HTTP 422), never silently truncated. decima-agent reads 2,048.
Training
decima-base starts from jhu-clsp/mmBERT-base (MIT) and was trained in two stages, 1.27M examples in the second:
- Teacher-labelled decisions in the System One format. The teachers are Gemma and Qwen3-Coder, run locally.
- Public NLI and classification train splits.
- Procedural decisions with exact labels, including grid and side-scroller scenes, reworded by an LLM under a "keep every number" check.
The synthetic parts are public:
- decima-system-one-tasks: 138k typed decisions in the Jev/TypeSafe schema, nine languages;
- decima-game-decisions: 330k grid, side-scroller and Snake decisions;
- decima-synthetic-decisions: the first-generation decisions from decima-small.
Every evaluation set was decontaminated against the training data. Licensing of the data: release/LICENSING.md.
Limitations
- Far from Jev on hard, unfamiliar decisions (jabr v2: 0.673 vs 0.966). Use it for narrow, well-defined questions with clear options, and send the fuzzy ones to a bigger model.
- Numbers and rules inside the text (thresholds, dates, counting) remain its weakest area, as for decima-small.
- Persian costs about 5 points against English on the same tasks (0.616 vs 0.673). Other languages are less tested.
- Slower than decima-small, which is the choice for browsers and the smallest CPUs; a short decision with four options takes about 47 ms on one CPU thread and 21 ms on four (ARM cores of an NVIDIA GB10), vs 14 / 7 ms for decima-small.
License and citation
Apache-2.0.
@software{madani2026decima,
author = {Madani, Amir Mahdi},
title = {Decima: open, calibrated decision models},
year = {2026},
url = {https://github.com/amyrmahdy/decima}
}