---
license: apache-2.0
base_model: google/diffusiongemma-26B-A4B-it
pipeline_tag: text-classification
tags:
- diffusiongemma
- structured-prediction
- classification
- multimodal
- custom-code
language:
- en
inference: false
---

# Jiffy v0.1.0

Experimental typed decisions over documents and images using frozen DiffusionGemma.

**This repository releases an inference adapter, not a newly trained checkpoint.**
It contains source code, an installable wheel, and evaluation evidence. There
are no Jiffy-specific model weights. The runtime downloads Google's original
[DiffusionGemma checkpoint](https://huggingface.co/google/diffusiongemma-26B-A4B-it)
at revision `f7f5b7f5fa82ffc52addd066915886d497f5517b`.

Jiffy is independent of Google and TypeSafe AI. It is not a certified Jev
replacement and does not claim equivalent calibration or accuracy.

## Use

Install from the [public GitHub release](https://github.com/kidzik/jiffy/releases/tag/v0.1.0)
or the wheel in this repository's `packages/` directory.
Full install instructions, HTTP API, image examples, and runtime limits:
[INSTALL.md](INSTALL.md).

```python
from jiffy import DiffusionDecisions, JevProtocol

model = DiffusionDecisions.from_backbone()
api = JevProtocol(model)
result = api.evaluate({
    "model": "jiffy-diffusiongemma",
    "state": {"message": "Please refund the duplicate charge."},
    "questions": {
        "refund": {"type": "noul", "instructions": "Is a refund requested?"}
    },
})
print(result["answers"])
```

The Hub repository itself cannot be loaded with `AutoModel.from_pretrained`.
Use Jiffy's API, which loads the upstream checkpoint. No hosted inference
endpoint is included.

## Method

Noul, Choice, and Score return bounded typed answers. Choice and Score expose
full distributions. The default adapter encodes the document once, forks
independent question caches, and batches equal-length branches. Score levels
are evaluated independently. A separate shared-pass image SDK is also provided;
that faster mode does not isolate questions.

## Evaluation

On 231 unchanged public JevBench tasks: **195 correct (84.4%)**, with 100%
valid responses. Easy: 48/48; standard: 68/72; hard: 79/111.
Published Jev scored 200/231 on those same IDs.
Local H100 latency: 259 ms median, 588 ms p95, excluding network.

This is not a complete 534-task benchmark score or an official ranking.
See [the evaluation report](docs/jevbench-public.md) and
`packages/jevbench-public-20260921.zip` for predictions and provenance.

## Requirements and Limitations

Validated on Linux, Python 3.10, H100 80GB, BF16. Observed GPU footprint:
approximately 50 GiB for a small image with four branches, and 69 GiB for
32K plus an image with four branches. No quantized, CPU, Apple GPU, or
multi-GPU runtime is supported.

Probabilities are uncalibrated (public hard-tier ECE 0.204). A mixed Score
fixture shifted by 0.0428 versus sequential inference, exceeding a 0.03
equivalence threshold. Short unequal-length requests can be slower.
Do not treat high confidence as verified evidence or use this experimental
release for consequential automated decisions without domain validation.

See [compatibility](docs/compatibility.md) and [release status](docs/release.md).
Code is Apache-2.0; dependencies and benchmark assets retain their own terms.
Google's original weights are distributed separately under their upstream terms.