Add the deck31b model card. Weights stay on google/gemma-4-31B-it.

README.md

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+---
+license: apache-2.0
+base_model: google/gemma-4-31B-it
+tags:
+  - decision
+  - text-classification
+---
+
+# deck31b
+
+deck31b serves frozen `google/gemma-4-31B-it` at revision
+`842da3794eaa0b77d5f08bae87a17459d91ff475`. This repository does not contain
+those weights. The server downloads them and quantizes linear layers to FP8
+e4m3 weights and activations at startup.
+
+We did not train the checkpoint. The prompt and letter readout follow the
+public Cygnet recipe (`blockbrain-ai/cygnet-recipe` at
+`81974de878a7385f878be828205c02064f8bc045`), with temperature `3.4`.
+
+Code: https://github.com/krishna-gogineni-765/deck31b
+
+```bash
+git clone https://github.com/krishna-gogineni-765/deck31b
+cd deck31b
+python -m venv .venv && source .venv/bin/activate
+python -m pip install --upgrade pip
+python -m pip install --index-url https://download.pytorch.org/whl/cu128 torch==2.11.0
+python -m pip install -e .
+deck31b-serve --host 0.0.0.0 --port 8090
+```
+
+`POST /v1/systemone` supports `noul`, `choice`, and `score`. The base weights
+remain under the Gemma Terms of Use. This server code is Apache-2.0.

config.json

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+{
+  "model_name": "deck31b",
+  "base_model": "google/gemma-4-31B-it",
+  "base_revision": "842da3794eaa0b77d5f08bae87a17459d91ff475",
+  "quantization": "torchao.Float8DynamicActivationFloat8WeightConfig float8_e4m3fn",
+  "temperature": 3.4,
+  "max_context_tokens": 16384,
+  "max_options": 26,
+  "readout": "cygnet letter mass, full softmax, p^(1/T)",
+  "trained": false
+}