diff --git a/docs_new/cookbook/autoregressive/Google/DiffusionGemma.mdx b/docs_new/cookbook/autoregressive/Google/DiffusionGemma.mdx
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+---
+title: DiffusionGemma
+metatags:
+ description: "Deploy DiffusionGemma with SGLang - Google's uniform-state renoising block-diffusion language model (26B-A4B MoE) served with the Gemma4Renoise sampler."
+tag: NEW
+---
+
+## 1. Model Introduction
+
+DiffusionGemma is a uniform-state (renoising) block-diffusion language model from Google. An encoder builds causal context, and a decoder denoises a fixed-length bidirectional canvas of `canvas_length` tokens. The `Gemma4Renoise` sampler runs `max_denoising_steps` reverse steps over the canvas, feeding the previous step's logits back as self-conditioning and emitting the greedy argmax of the processed logits.
+
+**Key Features:**
+
+- **Uniform-State Renoising**: The canvas starts from random tokens and is refined each step by accepting confident positions and re-noising the rest, with no mask token.
+- **Encoder / Decoder Canvas**: The encoder produces causal context KV, the decoder attends bidirectionally over the canvas.
+- **Self-Conditioning**: Each step conditions on the previous step's logits.
+- **EntropyBound Acceptance**: Each step accepts the lowest-entropy canvas positions within an entropy budget and re-noises the rest.
+- **StableAndConfident Stopping**: A canvas stops early once it is stable and confident.
+- **MoE Architecture**: The 26B-A4B model uses a Mixture-of-Experts architecture for efficient inference.
+- **Multimodal Input**: Accepts text and image inputs (via a ~550M vision encoder) and generates text output.
+
+**Available Models:**
+
+
+
+
+
+
+
+
+
+ | Model |
+ Architecture |
+ Parameters |
+
+
+
+
+ | [google/diffusiongemma-26B-A4B-it](https://huggingface.co/google/diffusiongemma-26B-A4B-it) |
+ MoE, uniform-state diffusion (text + image) |
+ 25.2B total / 3.8B active |
+
+
+
+
+**Architecture Specifications:**
+
+| Spec | Value |
+| --- | --- |
+| Total Parameters | 25.2B |
+| Active Parameters | 3.8B |
+| Layers | 30 |
+| Sliding Window | 1024 tokens |
+| Context Length | Up to 256K tokens |
+| Canvas Length | 256 |
+| Vocabulary Size | 262K |
+| Experts | 8 active / 128 total + 1 shared |
+| Supported Modalities | Text, Image |
+| Vision Encoder | ~550M parameters |
+
+**License:**
+
+Refer to the model card for license details.
+
+## 2. SGLang Installation
+
+Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
+
+The checkpoint ships its own modeling code, so `--trust-remote-code` is required when serving.
+
+## 3. Model Deployment
+
+### 3.1 Basic Configuration
+
+The required runtime settings are applied automatically for `Gemma4Renoise` (the Triton attention backend, eager mode, and unchunked prefill, needed because the full-attention head_dim is 512 and the canvas uses bidirectional attention), so a default launch works:
+
+```bash Command
+sglang serve \
+ --model-path google/diffusiongemma-26B-A4B-it \
+ --dllm-algorithm Gemma4Renoise \
+ --trust-remote-code \
+ --host 0.0.0.0 \
+ --port 30000
+```
+
+### 3.2 Configuration Tips
+
+**dLLM-Specific Parameters:**
+
+
+
+
+ | Parameter |
+ Description |
+ Recommended Value |
+
+
+
+
+ | `--dllm-algorithm` |
+ Diffusion decoding algorithm |
+ `Gemma4Renoise` |
+
+
+ | `--trust-remote-code` |
+ Required to load the checkpoint's modeling code |
+ Always enabled |
+
+
+ | `--dllm-algorithm-config` |
+ Optional YAML overriding the renoise schedule |
+ Checkpoint defaults |
+
+
+
+
+The attention backend, eager mode, and unchunked prefill are selected automatically for `Gemma4Renoise`, so they do not need to be passed on the command line.
+
+Sampling is governed by the renoise schedule. Request-level `logprobs`, penalties, `logit_bias`, and grammar / structured output (`json_schema` / `regex` / `ebnf` / `structural_tag`) are not applied and are rejected with a 400. Core sampling controls (`temperature`, `top_k`, `top_p`) are accepted but have no effect. Streaming is block-level: one fully-denoised canvas per chunk.
+
+**Gemma4Renoise Config** (defaults follow the checkpoint's `generation_config.json`):
+
+```yaml Config
+# Number of reverse denoising steps per canvas.
+max_denoising_steps: 48
+# Optional. Makes the renoise sampling reproducible (also shared across TP ranks).
+seed: 1234
+sampler_config:
+ # Entropy budget. Accept the lowest-entropy canvas positions within this bound each step (the rest are re-noised).
+ entropy_bound: 0.1
+# Linear temperature schedule applied over the denoising steps.
+temperature_schedule:
+ t_min: 0.4
+ t_max: 0.8
+# Stop early once the canvas is stable and confident.
+stopping_config:
+ confidence_threshold: 0.005
+ stability_threshold: 1
+```
+
+## 4. Model Invocation
+
+### 4.1 Deployment
+
+Start the server with the command from [Section 3.1](#31-basic-configuration).
+
+### 4.2 Basic Usage
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(
+ base_url="http://localhost:30000/v1",
+ api_key="EMPTY"
+)
+
+response = client.chat.completions.create(
+ model="google/diffusiongemma-26B-A4B-it",
+ messages=[
+ {"role": "user", "content": "What are the key differences between TCP and UDP?"}
+ ],
+ max_tokens=1024
+)
+
+print(response.choices[0].message.content)
+```
+
+### 4.3 Streaming
+
+Streaming emits one fully-denoised canvas per chunk.
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(
+ base_url="http://localhost:30000/v1",
+ api_key="EMPTY"
+)
+
+response = client.chat.completions.create(
+ model="google/diffusiongemma-26B-A4B-it",
+ messages=[
+ {"role": "user", "content": "Write a Python function to compute the Fibonacci sequence."}
+ ],
+ max_tokens=2048,
+ stream=True
+)
+
+for chunk in response:
+ if chunk.choices and len(chunk.choices) > 0:
+ delta = chunk.choices[0].delta
+ if delta.content:
+ print(delta.content, end="", flush=True)
+
+print()
+```
+
+## 5. Benchmark
+
+### 5.1 Speed Benchmark
+
+Not benchmarked for speed.
+
+### 5.2 Accuracy Benchmark
+
+Full test splits, every item scored (no failed-request exclusions). Text MCQ benchmarks use greedy generate-and-parse, MATH uses boxed-answer extraction plus sympy equivalence. MMLU, ARC-Challenge, and MATH-500 are the mean of two independent server launches.
+
+
+
+
+
+
+
+
+ | Benchmark |
+ Score |
+
+
+
+
+ | GSM8K |
+ 95.4% |
+
+
+ | ARC-Challenge |
+ 91.6% |
+
+
+ | HumanEval |
+ 92.7% pass@1 |
+
+
+ | MMLU |
+ 76.2% |
+
+
+ | MMLU-Pro |
+ 73.7% |
+
+
+ | GSM-Symbolic |
+ 92.2% |
+
+
+ | MATH-500 |
+ 72.1% |
+
+
+ | AIME-2026 |
+ 10.0% |
+
+
+ | HMMT-Feb-2025 |
+ 10.0% |
+
+
+ | GPQA-main |
+ 59.2% |
+
+
+
+
+Multimodal, full standard split per task (MMMU / MMMU-Pro / MMStar / AI2D as multiple-choice, MathVista testmini, DocVQA by ANLS, ChartQA by relaxed accuracy):
+
+
+
+
+
+
+
+
+ | Multimodal benchmark |
+ Score |
+
+
+
+
+ | MMMU (val, MC) |
+ 64.9% |
+
+
+ | MMMU-Pro (standard 10-opt, MC) |
+ 57.3% |
+
+
+ | MathVista (testmini) |
+ 68.4% |
+
+
+ | DocVQA (val) |
+ 85.9% |
+
+
+ | ChartQA (test) |
+ 61.7% |
+
+
+ | AI2D (test) |
+ 78.7% |
+
+
+ | MMStar (val) |
+ 65.9% |
+
+
+
diff --git a/docs_new/docs.json b/docs_new/docs.json
index 93a039688..d7c5bce42 100644
--- a/docs_new/docs.json
+++ b/docs_new/docs.json
@@ -976,7 +976,8 @@
{
"group": "Google",
"pages": [
- "cookbook/autoregressive/Google/Gemma4"
+ "cookbook/autoregressive/Google/Gemma4",
+ "cookbook/autoregressive/Google/DiffusionGemma"
]
},
{
diff --git a/docs_new/docs/supported-models/diffusion_language_models.mdx b/docs_new/docs/supported-models/diffusion_language_models.mdx
index 4dea53920..1f31d952e 100644
--- a/docs_new/docs/supported-models/diffusion_language_models.mdx
+++ b/docs_new/docs/supported-models/diffusion_language_models.mdx
@@ -129,5 +129,10 @@ Below the supported models are summarized in a table.
JetLM/SDAR-30B-A3B-Chat |
SDAR series diffusion language model (Chat), MoE architecture. |
+
+ | DiffusionGemma |
+ google/diffusiongemma-26B-A4B-it |
+ Uniform-state (renoising) block-diffusion multimodal (text + image) MoE, 25.2B total / 3.8B active, served with the Gemma4Renoise sampler. |
+