Instructions to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Use Docker

docker model run hf.co/FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

LM Studio
Jan
Ollama
How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4
```

Unsloth Studio

How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF to start chatting

How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Run Hermes

hermes

OpenClaw new

How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4
```

Lemonade

How to use FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF:MXFP4

Run and chat with the model

lemonade run user.DiffusionGemma-26B-A4B-it-MXFP4-GGUF-MXFP4

List all available models

lemonade list

Atomic Chat

DiffusionGemma 26B-A4B-it - MXFP4 GGUF

MXFP4 quantization of google/diffusiongemma-26B-A4B-it, a 26B parameter MoE diffusion model for text-to-image generation with 4B active parameters.

About the Model

DiffusionGemma is a block diffusion model built on top of the Gemma 4 architecture, designed for text-to-image generation.

26B total parameters with 4B active per token (128 experts, 8 active)
30-layer MoE decoder with sliding + full attention hybrid
27-layer vision encoder for image understanding
Text-to-image generation - generates images from text prompts
Block diffusion - iterative refinement approach to image generation

Quantization

This GGUF was quantized from the BF16 safetensors using llama.cpp (build 537). The source weights were converted to F16 GGUF, then quantized to MXFP4 format.

MXFP4 (Microscaling FP4) uses block-wise quantization with shared exponents per block.

Files

File	Size	Description
`diffusiongemma-26b-a4b-it-mxfp4.gguf`	~12.7 GB	MXFP4 quantized model weights
`mmproj-diffusiongemma-26b-a4b-f16.gguf`	~1.19 GB	Vision encoder (F16)

Usage

llama.cpp

llama-server \
  -m diffusiongemma-26b-a4b-it-mxfp4.gguf \
  --mmproj mmproj-diffusiongemma-26b-a4b-f16.gguf \
  -ngl 99 \
  --host 0.0.0.0 \
  --port 8080

Hardware Requirements

Minimum: 16 GB VRAM for partial offload
Recommended: 24+ GB VRAM for full GPU offload

License

Apache 2.0 - same as the base model.

Note: Vision support requires the mmproj file. The mmproj is shared between the NVFP4 and MXFP4 variants.

Downloads last month: 1,053

GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for FreedomAISVR/DiffusionGemma-26B-A4B-it-MXFP4-GGUF

Base model

google/diffusiongemma-26B-A4B-it

Quantized

(37)

this model