---
license: gemma
base_model: google/diffusiongemma-26B-A4B-it
pipeline_tag: image-text-to-text
tags:
- mlx
- vmlx
- osaurus
- diffusion-language-model
- block-diffusion
- mxfp4
- gemma
library_name: mlx
---

![Osaurus](osaurus-x-banner.png)

# DiffusionGemma 26B-A4B-it — MXFP4 (Osaurus / vMLX)

Native MLX MXFP4 quantization of `google/diffusiongemma-26B-A4B-it` — a
**block-diffusion** language model (NOT autoregressive): text generates as
256-token canvases refined by iterative denoising. 30-layer Gemma-4-style
MoE, 128 experts top-8, 26B total / ~4B active parameters.

Runs natively in [Osaurus](https://github.com/osaurus-ai/osaurus) on Apple
Silicon via the vmlx-swift block-diffusion engine.

## Quantization

- Attention + routed MoE experts: **MXFP4** (group 32)
- Dense MLP + router projections: MXFP8 (group 32)
- Embeddings, norms, self-conditioning, vision tower: fp16 passthrough
- 15 shards, ~15 GB on disk, peak runtime RSS ≈ 12.7 GB (M5 Max)

## Capabilities

| | |
|---|---|
| Text generation | ✅ block diffusion (~37 tok/s @ bundle default 48 steps, ~74 tok/s @ 16 steps, M5 Max) |
| Vision (single/multi image) | ✅ Gemma-4 unified vision tower, 280 soft tokens/image |
| Tool calling | ✅ Gemma-4 format `<\|tool_call>call:name{...}<tool_call\|>` |
| Reasoning channel | ✅ harmony `<\|channel>thought…<channel\|>` |
| Audio | ❌ not in this checkpoint (no audio_config) |
| Video | ❌ no `video_token_id` |

## Generation contract

All diffusion sampling parameters live in `generation_config.json` and are
honored by the runtime: `max_denoising_steps=48`, entropy bound 0.1,
temperature schedule 0.8→0.4, stability 1, confidence 0.005,
`eos_token_id=[1, 106, 50]`, pad 0. Wire `temperature`/`top_p` are ignored
by design — the denoising schedule is bundle-owned. Speed/quality is
controlled by the denoising-step budget (Osaurus exposes this as a server
setting, default 16 ≈ 2× faster than the bundle default and verified
coherent; below 12 quality degrades).

The chat template (`chat_template.jinja`) ships in this repo, including
tool-call and thinking-channel rendering.

## Known behavior

Very terse prompts under greedy denoising can occasionally converge to an
empty (EOS-first) canvas — inherent to the reference sampling algorithm
with random canvas initialization; retry or rephrase.