> ## Documentation Index > Fetch the complete documentation index at: https://docs.sglang.io/llms.txt > Use this file to discover all available pages before exploring further. # DiffusionGemma ## 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 theme={null} 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 theme={null} # 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 theme={null} 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 theme={null} 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%