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# DiffusionGemma

[DiffusionGemma](https://huggingface.co/google) is a block-diffusion language model from Google. Instead of generating tokens left-to-right, it denoises a fixed-length canvas of tokens in parallel: a causal encoder reads the prompt and a bidirectional decoder iteratively refines the response canvas. The released checkpoint is a Mixture-of-Experts model with 26B total parameters and \~4B active per token.

> **Info**
>
> |                  |                                         |
> | ---------------- | --------------------------------------- |
> | **Task**         | Text Generation (Block Diffusion, MoE)  |
> | **Architecture** | `DiffusionGemmaForBlockDiffusion`       |
> | **Parameters**   | 26B total / \~4B active                 |
> | **HF Org**       | [google](https://huggingface.co/google) |

## Available Models

* **DiffusionGemma 26B-A4B-it** (`DiffusionGemmaForBlockDiffusion`): instruction-tuned block-diffusion MoE.

## Architectures

* `DiffusionGemmaForBlockDiffusion` — block-diffusion MoE (causal prompt encoder + bidirectional canvas decoder).

## Example HF Models

| Model                     | HF ID                                                                                         |
| ------------------------- | --------------------------------------------------------------------------------------------- |
| DiffusionGemma 26B-A4B-it | [`google/diffusiongemma-26B-A4B-it`](https://huggingface.co/google/diffusiongemma-26B-A4B-it) |

## Example Recipes

| Recipe                                                                                                                                    | Description                                                       |
| ----------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------- |
| [diffusion\_gemma\_sft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/../../../../examples/dllm_sft/diffusion_gemma_sft.yaml)   | Full SFT — DiffusionGemma 26B-A4B with FSDP2 + Expert Parallelism |
| [diffusion\_gemma\_lora.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/../../../../examples/dllm_sft/diffusion_gemma_lora.yaml) | LoRA SFT — DiffusionGemma 26B-A4B with FSDP2 + Expert Parallelism |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

> **Note**
>
> This recipe was validated with **Expert Parallelism (EP=8)** on a single 8×H100 node. See the [Launcher Guide](/job-launchers/slurm-cluster) for multi-node setup.

**3. Run the recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/dllm_sft/diffusion_gemma_sft.yaml
```

## Fine-Tuning

See the [DiffusionGemma Fine-Tuning Guide](/recipes-e2e-examples/diffusiongemma) for the block-diffusion training objective, self-conditioning, and the full list of supported features (SFT, LoRA, Expert Parallelism, activation checkpointing).

## Hugging Face Model Cards

* [google/diffusiongemma-26B-A4B-it](https://huggingface.co/google/diffusiongemma-26B-A4B-it)
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