Update model card

README.md

@@ -1,70 +1,73 @@
----
-license: apache-2.0
-base_model: FINAL-Bench/Darwin-35B-A3B-Opus
-library_name: transformers
-pipeline_tag: image-text-to-text
-tags:
-- vision
-- decision
-- multiple-choice
-- classification
-- moe
-- qwen3_5_moe
----
-
-# PhysicalEye-Decide-35B
-
-**One forward pass. One calibrated decision.**
-
-PhysicalEye-Decide-35B answers a question about an image by choosing among the options you give it, and returns a probability for every option. It does not write text. It reads the image, the question and the options once, and outputs a decision.
-
-Use it for visual inspection, yes/no checks, option selection and scoring on production lines, in apps and in robots.
-
-## Results
-
-| Test | Result |
-|---|---|
-| Egg inspection, held-out test (100 photos, good vs bad) | **98.0%** accuracy, mean confidence 0.964 |
-| AI2D diagram questions (200, 4 options) | **87.0%** (chance 25%) |
-| Latency per decision, median (one B200 GPU) | **0.051 s** (p80 0.068 s) |
-
-## How it works
-
-- Backbone: a vision-language Mixture-of-Experts model (35B total, about 3B active per token).
-- A small decision readout sits on top of the backbone and scores each option directly.
-- Output: one probability per option. Probabilities are temperature-calibrated.
-
-## Input format
-
-Each request has an optional image, a question and a list of options.
-
-```json
-{
-  "images": ["<base64 or URL>"],
-  "question": "Is this egg good or bad?",
-  "options": ["good", "bad"]
-}
-```
-
-The model returns the probability of each option, for example `{"good": 0.03, "bad": 0.97}`.
-
-## Files
-
-- `model-*.safetensors`: backbone weights
-- `readout.safetensors`: decision readout
-- `decision_config.json`: option codes, token ids and calibration temperature
-
-## Base model and license
-
-- Built on [FINAL-Bench/Darwin-35B-A3B-Opus](https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus), a Qwen3.5 MoE based model.
-- Released under Apache-2.0. The upstream Qwen license notice applies to the base weights.
-
-## Training data credits
-
-The decision readout was trained on openly licensed data:
-
-- VQAv2 (CC BY 4.0, annotations), COCO captions and instances 2017 (CC BY 4.0, annotations; images keep their Flickr licenses)
-- TextVQA 0.5.1 (CC BY 4.0), ScreenQA and RICO-SCA / Screen2Words on RICO (CC BY 4.0)
-- CLEVR v1.0 (CC BY 4.0), A-OKVQA (Apache-2.0), VSR (CC BY 4.0), CORD v2 (CC BY 4.0)
-- Open-Jev (CC0 1.0), MMLU auxiliary train (MIT), synthetic charts made in-house
-- Good and Bad Eggs Identification Image Dataset, Md Mafiul Hasan Matin, Marium Jahan, Md. Saroar Jahan, Mendeley Data, doi:10.17632/mdty358x8m.1 (CC BY 4.0)
+---
+license: apache-2.0
+base_model: FINAL-Bench/Darwin-35B-A3B-Opus
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- vision
+- decision
+- multiple-choice
+- classification
+- moe
+- qwen3_5_moe
+---
+
+# PhysicalEye-Decide-35B
+
+**One forward pass. One calibrated decision.**
+
+PhysicalEye-Decide-35B answers a question about an image by choosing among the options you give it, and returns a probability for every option. It does not write text. It reads the image, the question and the options once, and outputs a decision.
+
+Use it for visual inspection, yes/no checks, option selection and scoring on production lines, in apps and in robots.
+
+## Results
+
+| Test | Result |
+|---|---|
+| Egg inspection, held-out test (100 photos, good vs bad) | **98.0%** accuracy, mean confidence 0.964 |
+| AI2D diagram questions (200, 4 options) | **87.0%** (chance 25%) |
+| Latency per decision, median (one B200 GPU) | **0.051 s** (p80 0.068 s) |
+
+## How it works
+
+- Backbone: a vision-language Mixture-of-Experts model (35B total, about 3B active per token).
+- A small decision readout sits on top of the backbone and scores each option directly.
+- Output: one probability per option. Probabilities are temperature-calibrated.
+
+## Input format
+
+One request carries up to 4 images, an optional context (`state`) and one or more questions.
+Three question types are supported: `choice` (pick one option), `noul` (yes or no) and `score` (a value on a scale).
+
+```json
+{
+  "images": ["<base64 image>"],
+  "state": "Egg inspection line, candling light.",
+  "questions": {
+    "q1": {"type": "choice", "question": "Is this egg good or bad?", "options": ["good", "bad"]}
+  }
+}
+```
+
+The response has one entry per question key in `answers`, with the chosen option and its probability.
+
+## Files
+
+- `model-*.safetensors`: backbone weights
+- `readout.safetensors`: decision readout
+- `decision_config.json`: option codes, token ids and calibration temperature
+
+## Base model and license
+
+- Built on [FINAL-Bench/Darwin-35B-A3B-Opus](https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus), a Qwen3.5 MoE based model.
+- Released under Apache-2.0. The upstream Qwen license notice applies to the base weights.
+
+## Training data credits
+
+The decision readout was trained on openly licensed data:
+
+- VQAv2 (CC BY 4.0, annotations), COCO captions and instances 2017 (CC BY 4.0, annotations; images keep their Flickr licenses)
+- TextVQA 0.5.1 (CC BY 4.0), ScreenQA and RICO-SCA / Screen2Words on RICO (CC BY 4.0)
+- CLEVR v1.0 (CC BY 4.0), A-OKVQA (Apache-2.0), VSR (CC BY 4.0), CORD v2 (CC BY 4.0)
+- Open-Jev (CC0 1.0), MMLU auxiliary train (MIT), synthetic charts made in-house
+- Good and Bad Eggs Identification Image Dataset, Md Mafiul Hasan Matin, Marium Jahan, Md. Saroar Jahan, Mendeley Data, doi:10.17632/mdty358x8m.1 (CC BY 4.0)