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decider

3 TagsUpdated Apache-2.0by Mapika

Decoder decision models by Mapika on Qwen3.5: the answer is read from option-letter logits in one forward pass. The most accurate open decision model Ollaya ships.

decisionlong-context0.75b1.9b
ollaya run decider --preset triage "I was charged twice for my subscription this month and want a refund."

Models

View all
Name
deciderlatest3.8 GB · 32768 ctx · English
decider:0.8b1.5 GB · 32768 ctx · English
decider:2b3.8 GB · 32768 ctx · English

Each model carries fp16 and fp32 graphs over one weights file, and loads fp16 on a CUDA GPU and fp32 on CPU.

Readme

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

TagBaseParamsTyped-decisions accuracy
decider:latest, decider:2bQwen3.5-2B1.9B0.591
decider:0.8bQwen3.5-0.8B0.75B0.506

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.

Usage

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

Speed

  • RTX 4090, end to end: a five-question request with a short state takes about 155 ms on 0.8b and 190 ms on 2b at the median. That is slower than the encoder models (Laya: 8–10 ms), but 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 MB). The BF16 weights are widened to fp32 when the model loads.
  • 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. 2b needs about 8 GB of memory at fp32. Both models are slow on the CPU.
  • Score criteria. Criteria given as an object instead of a list are rejected, as TypeSafe's API does.