---
license: apache-2.0
base_model: google/diffusiongemma-26B-A4B-it
base_model_relation: finetune
pipeline_tag: text-classification
tags: [jev, systemone, typed-decisions, decision-model, diffusion-lm, calibration, nvfp4]
---
# super-decider

**What it does.** Given a state (text, JSON or images) and typed questions, the model returns one calibrated answer per
question in a single read: `choice` (a probability per option), `noul` (P(yes)) and `score` (expected level over an
ordered rubric, with the distribution).

## Model
- **Base:** `google/diffusiongemma-26B-A4B-it` (discrete-diffusion MoE, 26B total, 4B active), served from NVIDIA's
  NVFP4 checkpoint `nvidia/diffusiongemma-26B-A4B-it-NVFP4`.
- 18 GB NVFP4 checkpoint (experts 4-bit). No benchmark test items were used for training.
- `generation_config.json` sets `confidence_threshold` 0.3 for thought canvases.
- Served with contexts up to 131,072 tokens.

## How it works
- **Fine-tuned for decisions.** DiffusionGemma 26B-A4B is fine-tuned on permissively licensed and synthetic decision
  data, then merged into NVIDIA's NVFP4 checkpoint.
- **One-step reads.** All answer slots of a request are filled in one denoising step, so several questions cost little
  more than one.
- **Bidirectional attention within each question.** The prompt block of every question is read in both directions, not
  only left to right. The model was trained this way, and the runtime applies the same attention through a small vLLM
  patch (`DJEV_BIDIR_SERVE=1`, `DJEV_BIDIR=qsep`).
- **Adaptive thinking.** When the model is unsure of an answer, it writes a short thought and reads the answer again.
  A request can turn this off, keep it on under load, or think on every question with `"thinking"` (see
  [API docs](https://github.com/Singularity-Compute/super-decider/blob/main/docs/API.md)).

## Serving
Runtime: [Singularity-Compute/super-decider](https://github.com/Singularity-Compute/super-decider) (pinned vLLM nightly,
vLLM patches and a structured server). It accepts the Jev / System One request format (`POST /v1/systemone`) and
OpenAI-compatible chat completions. One 48 GB GPU is enough; on L40S the NVFP4 weights run through the Marlin W4A16
kernel.

## Limitations
- Answers can change when the input changes in ways that keep its meaning, such as JSON key order or option order.
- The model is sometimes confidently wrong on multi-step date and money calculations; thinking on every question fixes
  part of them.
- The training data is English-centric. Images go through the base model's vision path without image fine-tuning.

## Licence
Apache-2.0, like the base model. See [NOTICE.md](NOTICE.md).