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 "git+https://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)

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}
}