The smallest decider: a language model that does not generate text. It reads a **state** (a string or any JSON value) and a set of **typed questions** and returns a probability distribution for every question from a single forward pass: Choice (2-255 options, optionally described), Score (2-10 described levels), Noul (probability of yes). No decoding, no parsing, no output outside the options you defined. Same code, same wire format (`POST /v1/systemone`, TypeSafe Jev's format), same training recipe as the 2B: one epoch of `scripts/train.sh full` from `Qwen/Qwen3.5-0.8B-Base` over the full mixture (1.47M examples, 455M tokens, 4.5 h on one GH200). All six repositories share one interface (`decider.infer.Decider`, `POST /v1/systemone` in TypeSafe's format) and one readout: the letter logits at an answer slot, softmaxed over the options. Pick by size and input. Code, data registry, training scripts, the changelog and the per-version history: https://github.com/Mapika/decider.
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A language model that does not generate text. It reads a state and one or more typed questions, each with an explicit option list, and returns a probability distribution over the options for every question from one forward pass. It is called from software, not chatted with. From v12 the state may include images. **This repository holds v12 (2026-10-20).** v11 stays available under the Hub tag `v11`, with its own card; v10 under `v10`. - **Weights:** the text model at the repository root (Qwen3.5-2B-Base architecture, bf16, 3.8 GB), loaded as before by `decider.infer.Decider`, `decider.serve` and the GGUF builds. `vision/` holds the image part: the vision tower of Qwen3.5-2B-Base (unchanged, 0.6 GB) and the config of the image-text model. The image path reuses the text model's weights. - **Code:** decider-ai 1.10.0 for images; text works with 1.4.0 and later.
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GGUF files of Mapika/decider-2b v12, for llama.cpp. decider-2b does not generate text. It reads a state and one or more questions, each with an explicit option list, and returns a probability for every option from one forward pass. See the decider-2b card for what the model is, how it was trained, and where it is weak. **This repository holds the v12 files (2026-10-20).** The v11 files stay available under the Hub tag `v11`. The GGUF files hold the text model only: image input needs the PyTorch weights of Mapika/decider-2b and decider-ai 1.10.0. `decider_config.json` reads yes/no (Noul) answers at temperature 0.05, as the model's own config does (see its card); `decide_gguf.py` reads every question as Choice, at the Choice temperature. The tokenizer, `decider_config.json` (temperatures) and `decide_gguf.py` (the readout on llama.cpp) are in this repository too.
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A language model that does not generate text. It reads a state and one or more typed questions, each with an explicit option list, and returns a probability distribution over the options for every question from one forward pass. It is called from software, not chatted with. From v3 the state may include images. **This repository holds v3 (2026-10-20).** v2.1 stays available under the Hub tag `v2.1`, with its own card; v2 and v1 under `v2` and `v1`. - **Weights:** the text model at the repository root (Qwen3.5-4B-Base architecture, bf16, 8.4 GB), loaded as before by `decider.infer.Decider`, `decider.serve` and the GGUF builds. `vision/` holds the image part: the vision tower of Qwen3.5-4B-Base (unchanged, 0.6 GB) and the config of the image-text model. The image path reuses the text model's weights. - **Code:** decider-ai 1.10.0 for images; text works with 1.4.0 and later.
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GGUF files of Mapika/decider-4b v3, for llama.cpp. decider-4b does not generate text. It reads a state and one or more questions, each with an explicit option list, and returns a probability for every option from one forward pass. See the decider-4b card for what the model is, how it was trained, and where it is weak. **This repository holds the v3 files (2026-10-20).** The v2.1 files stay available under the Hub tag `v2.1`. The GGUF files hold the text model only: image input needs the PyTorch weights of Mapika/decider-4b and decider-ai 1.10.0. `decider_config.json` reads yes/no (Noul) answers at temperature 0.05, as the model's own config does (see its card); `decide_gguf.py` reads every question as Choice, at the Choice temperature. The tokenizer, `decider_config.json` (temperatures) and `decide_gguf.py` (the readout on llama.cpp) are in this repository too.
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