decider

decider is a family of open decision models by Mapika, built on Qwen3.5 base models and released under Apache-2.0. For each question it lays out the context, the question and lettered options, then reads the model's next-token logits for the option letters at an answer slot. It never generates text; every question is one forward pass.

Models

Accuracy is the argmax against the majority label on all 400 typed-decisions states. For comparison, nli scores 0.548, gliclass 0.477 and laya:en 0.361. The labels have low annotator agreement, so compare the numbers against each other rather than reading them as absolutes. On Mapika's own benchmarks 4b is ahead of 2b (in-task 0.831 vs 0.805, held-out 0.784 vs 0.755, JevBench hard 0.649 vs 0.459), and the Decision Index scores it 36.6 against 26.1.

decider:4b needs Ollaya 0.7.0 or newer, which keeps its weights BF16 in memory. Older versions widen them to fp32 (about 17 GB).

Usage

ollaya run decider --preset triage "My order never arrived and support ignores me. Refund me today or I'm switching to your competitor."
ollaya run decider:4b --preset triage "My order never arrived and support ignores me. Refund me today or I'm switching to your competitor."

Images: decider:2b-vision

decider:2b-vision answers questions about an image as well as the state: a photo, a screenshot, a diagram or a game frame. Pass a PNG with --image, or base64 in images on /api/decide:

ollaya run decider:2b-vision --image shelf.png --questions '{"blocked":{"type":"noul","instructions":"Is the aisle blocked?"}}' "A photo from the warehouse camera."

• One PNG per request, up to about one megapixel (1024x1024). The model's own preprocessing (Qwen's resize to multiples of 32 pixels, PIL's bicubic filter, normalization) is reproduced value for value, so a JPEG has to be converted to PNG first: Rust's JPEG decoders differ from the one the model's authors use by a few levels on some pixels.
• Up to 10 options per question (v5's lettered prompt, A to J). Text-only requests work too.
• Parity. On 404 test questions, 12 of them with images: identical resized pixels, token ids and positions, the same decision on every question, and option logits within 1.6e-4 of the Python reference, on CPU and CUDA.
• Speed. On an RTX 4090, five questions about a 256x240 image take about 200 ms, and about 0.9 s about a 1000x750 one. On the CPU, 3 to 16 seconds.
• /v1/systemone takes no images: TypeSafe's API has no field for them.

decider:2b-vision needs Ollaya 0.7.5 or newer.

Speed

• RTX 4090, end to end: a five-question request with a short state takes about 155 ms on 0.8b, 190 ms on 2b and 520 ms on 4b at the median. That is slower than the encoder models (Laya: 8–10 ms). 0.8b and 2b are still faster than hosted Jev (236–276 ms).
• State length: cost grows with the number of options times the context length.
• Memory: on a 24 GB GPU, 2b handles states up to about 8k tokens. Longer states spill out of GPU memory and become very slow.

How it works

• Weights. They come from Mapika's own model.safetensors, downloaded from Hugging Face, pinned to a commit and verified by sha256. Ollaya hosts only the ONNX graph (8 to 11 MB).
• Precision. Every model computes in fp32 on the BF16 weights. 0.8b and 2b widen the weights to fp32 when they load. 4b keeps them BF16, half the memory, and widens each layer just before it runs; its answers are the same, and a forward pass takes about 12% longer.
• Calibration. 4b (v2.1) has one fitted temperature per answer type: choice 1.110, yes/no 1.560, score 1.287. 2b and 0.8b have one each.
• Parity. Ollaya's Rust runtime matches the Python reference exactly: identical token ids and answer-slot positions, and the same decision on every test question, on CPU and CUDA.

Limits

• Memory. On the GPU, with requests of about 1,400 tokens, 2b took about 13 GB and 4b about 14 GB (4b keeps its 8.4 GB of weights BF16). All three are slow on the CPU, where 4b peaked at 21 GB on the same requests; 4b belongs on a GPU.
• Score criteria. Criteria given as an object instead of a list are rejected, as TypeSafe's API does.
• Instructions. Every question needs instructions. Mapika's newer package also accepts a yes/no question without them; Ollaya rejects it.