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An open decision model built on Gemma 4 26B-A4B, a mixture of experts with 26B parameters and about 4B active per token. You send a state and typed options, and it returns a probability for every option. It is served through a Jev-compatible `POST /v1/systemone` API on your own GPU. - **Probabilities, not text:** you get a probability for every option and have nothing to parse. - **Decision Index 0.3: 62.30**, complete public suite, one pass (our run with the official 0.3 kit). Jev 1.13 scores 57.96. Auto scores ≈ 65.9 (estimate from a 14 % sample; Auto exceeds the 1,000 ms admission latency, so one pass is the default). - **JevBench-XL TEST: 75.3 %** (Jev 1.13: 67.85 %). - **JevBench public set: 219 / 231** (macro 95.3 %). Jev 1.13 gets 200 / 231. - **Optional thinking:** an Auto mode can use Gemma 4's native thinking when the top probability is below 0.7; the default is one pass. - **Fast:** median **53 ms** in one pass, measured serially with one request in flight on a warm server with CUDA graphs. This is processing time on one RTX PRO 6000, over 200 Decision Index 0.3 requests. - **Open:** Apache-2.0, BF16 weights (52 GB), served on one 96 GB GPU. 62.30 is our run of the complete public suite (140,620 requests, all answered) with the official 0.3 kit, in one pass, the default setting.

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Wald-4B

org2ai

An open 4B decision model: send a state and typed options, get a calibrated probability for every option. Served through a Jev-compatible `POST /v1/systemone` API on your own GPU. - **Probabilities, not text:** a calibrated probability for every option, nothing to parse. - **Decision Index 0.3: 60.05** on the complete public suite (our run; v2.0: 54.18). - **Thinks only when unsure:** Auto 0.7 thinks natively on about half of the requests. - **Fast:** median **158 ms** (serial, one request in flight, warm, processing time, one RTX PRO 6000, 200 DI 0.3 requests). - **Web actions:** Mind2Web element selection **92.3 %** (test splits, 6 candidates; v2.0: 84.6 %). - **Reads images and video** zero-shot through Qwen3.5-4B's unchanged vision tower. Video (wald-serve 0.2.2): **87.4 %** on VisualDecisionBench video (7,143 questions, our run), 2.5 s for a 15-second clip with 24 questions (serial, one RTX 5090). - **Small and open:** 4B, Apache-2.0. Self-run, not leaderboard results; how each number was produced: evaluation/v2.1/. DI 0.3 run files: org2ai/Wald-Q4B-decision-index-results.

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GGUF files of Wald-Q4B v2.1 (tag `v2.1`) for llama.cpp: an open 4B decision model that returns a calibrated probability for every option. - One pass, text only: the bundled server reads a probability for every option from the model's option-letter logits (Qwen chat template), calibrated with the same frozen temperature table as the BF16 release. - `--effort auto` (Auto 0.7) also runs here but has not been parity-checked on GGUF; for images, use the BF16 release. - On a 1,000-request Decision Index sample (5,922 questions), Q8_0 gives the BF16 answer on 99.5 % of questions and Q4_K_M on 97.3 %. All numbers: evaluation/v2.1-gguf-parity.json. - v2 (`Wald-4B-v2-*.gguf`, `v2/`): Wald-Q4B v2, unchanged in this repository. - v1.2 (`Wald-4B-v1.2-*.gguf`, `server/`): unchanged in this repository.

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