Instructions to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Use Docker

docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

LM Studio
Jan

vLLM

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aogavrilov/diffusiongemma-agent-iq3-cuda13"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aogavrilov/diffusiongemma-agent-iq3-cuda13",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Ollama
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Ollama:
```
ollama run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
```

Unsloth Studio

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aogavrilov/diffusiongemma-agent-iq3-cuda13 to start chatting

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

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": "aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

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 "aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M" \
  --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 aogavrilov/diffusiongemma-agent-iq3-cuda13 with Docker Model Runner:
```
docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
```

Lemonade

How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M

Run and chat with the model

lemonade run user.diffusiongemma-agent-iq3-cuda13-Q4_K_M

List all available models

lemonade list

diffusiongemma-agent-iq3-cuda13 / payload /app /docs /portable_bundle.md

aogavrilov

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	# Portable Bundle

	Build the self-contained IQ3 package on the working machine:

	```powershell
	powershell -ExecutionPolicy Bypass -File .\portable\Build-DiffusionGemmaAgentBundle.ps1 -Archive
	```

	It creates `dist/diffusiongemma-agent-portable` and an optional tar archive.
	The package includes the 12.4 GB IQ3 GGUF, known working CUDA runtime binary,
	CUDA 13 runtime libraries, server, and agent code. It excludes logs, virtual environments, source build
	trees, OpenCode, and optional agent frameworks.

	On another Windows machine with NVIDIA driver, WSL2, and Ubuntu already
	available, extract and install in one line:

	```powershell
	tar -xf .\diffusiongemma-agent-portable.tar; powershell -ExecutionPolicy Bypass -File .\diffusiongemma-agent-portable\Install-DiffusionGemmaAgent.ps1
	```

	The installer copies runtime files to WSL ext4, creates Python runtime with
	FastAPI, Aider, and Haystack, then starts backend plus safe proxy. Run a task:

	```powershell
	& .\diffusiongemma-agent-portable\dg.ps1 -Repo C:\work\repo -Task "Fix src/x.py and run pytest -q" -File src/x.py
	```

	The target needs about 14 GB free in WSL and about 16 GB VRAM. During transfer
	and installation, allow about 38 GB while tar, extracted files, and WSL copy
	coexist. The CUDA Toolkit is bundled. NVIDIA driver and WSL2 remain prerequisites.