Mapika
/

decider-2b-vision

@@ -15,6 +15,23 @@ calibrated probability over the options at a single answer slot. No generation.
 frame costs 64 visual tokens. Text-only questions work too, with decider-2b v5's behaviour,
 including its abstention handling.
 ## Usage
 ```python
@@ -35,9 +52,11 @@ MiniGrid and Super Mario Bros labelled by scripted policies (rare actions oversa
 DAgger frames from an earlier model's own play); multiple-choice image tasks from The Cauldron
 (A-OKVQA, AI2D, ScienceQA, IconQA, TQA, Raven, Hateful Memes); a replay of the text mixture.
 Then PPO from pixels on Breakout and Pong (the softmax over action options is the policy).
-Code: https://github.com/Mapika/decider
-## Results (300 items per task)
 | task | accuracy | ECE |
 |---|---|---|
@@ -51,4 +70,22 @@ teacher scores 22), Pong 3 (teacher 8), CliffWalking -13 (optimal), MiniGrid Emp
 (teacher level); held-out Freeway 0, FrozenLake 0, the harder grid worlds 0 (their scripted
 teachers also score 0), Mario 1-1 315 px. The previous vision release (v4-based) scored
 Breakout 16, Pong 8, Freeway 8, BabyAI-GoTo 0.30; this one trades Pong and Freeway for
-Breakout and for the corrected abstention behaviour. Not intended as a chat model.

 frame costs 64 visual tokens. Text-only questions work too, with decider-2b v5's behaviour,
 including its abstention handling.
+**Contents:** [The decider family](#the-decider-family) · [Usage](#usage) · [Training](#training) · [Results](#results) · [Limitations](#limitations) · [Changelog](#changelog)
+## The decider family
+All five 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.
+| model | base | weights | use it for | numbers |
+|---|---|---|---|---|
+| [decider-2b](https://huggingface.co/Mapika/decider-2b) v10 | Qwen3.5-2B-Base | 3.5 GB bf16 | the default: routing, classification, judgments, browser agents; 4 ms per request with CUDA graphs on one GPU | regression set 0.805 in-task / 0.755 held-out; live browser 93%; Bespoke suite 0.704 |
+| [decider-35b-a3b](https://huggingface.co/Mapika/decider-35b-a3b) v1 | Qwen3.5-35B-A3B-Base (3B active) | 65 GB bf16 | when accuracy is worth 3 to 4 times the cost per decision: knowledge and multi-step questions, long policies | 0.855 / 0.810, above the 2B on 93 of 95 tasks; JevBench hard 0.676; Bespoke 0.774; no RL stage |
+| [decider-35b-a3b-nvfp4](https://huggingface.co/Mapika/decider-35b-a3b-nvfp4) | the 35B in NVFP4 | 19.6 GB | the 35B on Blackwell through vLLM or TensorRT-LLM | 1.0 to 1.5 points under bf16 on the measured fixtures |
+| [decider-0.8b](https://huggingface.co/Mapika/decider-0.8b) | Qwen3.5-0.8B-Base | 1.4 GB bf16 | the smallest: routing, yes/no and short-state lookups within 1 to 4 points of the 2B, 1.5x faster | 0.776 / 0.707 on the single-run protocol (2B: 0.809 / 0.739) |
+| [decider-2b-vision](https://huggingface.co/Mapika/decider-2b-vision) | Qwen3.5-2B vision-language, v5 text weights | 4.1 GB bf16 | decisions from an image plus a question; game frames | Visual7W 0.89; Breakout 41 from pixels |
+Code, data registry, training scripts, the changelog and the per-version history: https://github.com/Mapika/decider.
 ## Usage
 ```python
 DAgger frames from an earlier model's own play); multiple-choice image tasks from The Cauldron
 (A-OKVQA, AI2D, ScienceQA, IconQA, TQA, Raven, Hateful Memes); a replay of the text mixture.
 Then PPO from pixels on Breakout and Pong (the softmax over action options is the policy).
+Code: https://github.com/Mapika/decider (`decider/vision/`).
+## Results
+300 items per task.
 | task | accuracy | ECE |
 |---|---|---|
 (teacher level); held-out Freeway 0, FrozenLake 0, the harder grid worlds 0 (their scripted
 teachers also score 0), Mario 1-1 315 px. The previous vision release (v4-based) scored
 Breakout 16, Pong 8, Freeway 8, BabyAI-GoTo 0.30; this one trades Pong and Freeway for
+Breakout and for the corrected abstention behaviour.
+## Limitations
+Not a chat model and not a captioner: it answers lettered options at one slot. The text weights inside are decider-2b **v5**,
+so the text-only behaviour is that of v5 (its abstention handling, none of the v6 to v10 input shapes, calibration or browser
+results); a retrain on the current text weights has not been released. Game play from pixels is measured on the Atari, MiniGrid
+and Mario frames it was trained on plus a few held-out games, three episodes each. English only; calibration is measured on the
+listed datasets, not on your images.
+## Changelog
+| version | what changed |
+|---|---|
+| **current weights** | v5 text weights transplanted into the Qwen3.5-2B vision-language model, one epoch on game frames, The Cauldron multiple-choice tasks and a text replay, then PPO from pixels on Breakout and Pong |
+| previous release | v4-based: Breakout 16, Pong 8, Freeway 8, BabyAI-GoTo 0.30 |
+Every decider release is listed in [docs/CHANGELOG.md](https://github.com/Mapika/decider/blob/main/docs/CHANGELOG.md) of the
+GitHub repository.