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 /scripts /dg_agent.sh

aogavrilov

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	#!/usr/bin/env bash
	set -euo pipefail

	export DG_AGENT_CALLER_CWD="${DG_AGENT_CALLER_CWD:-$PWD}"

	cd "$(dirname "$0")/.."

	if [[ -n "${DG_AGENT_PYTHON:-}" && -x "${DG_AGENT_PYTHON}" ]]; then
	exec "${DG_AGENT_PYTHON}" scripts/dg_agent.py "$@"
	fi

	# WSL must not fall through to the Windows .exe below: doing so makes the
	# supervisor launch the Linux Aider runner through Windows process semantics.
	if [[ "$(uname -s 2>/dev/null \|\| true)" == "Linux" ]]; then
	WSL_AGENT_PYTHON="${DG_WSL_AGENT_PYTHON:-/root/diffusiongemma-agent/.venv-wsl/bin/python}"
	if [[ -x "${WSL_AGENT_PYTHON}" ]]; then
	export DG_AGENT_PYTHON="${WSL_AGENT_PYTHON}"
	exec "${WSL_AGENT_PYTHON}" scripts/dg_agent.py "$@"
	fi
	fi

	if [[ -x .venv/bin/python ]]; then
	exec .venv/bin/python scripts/dg_agent.py "$@"
	fi

	if [[ -x .venv/Scripts/python.exe ]]; then
	exec .venv/Scripts/python.exe scripts/dg_agent.py "$@"
	fi

	if command -v python >/dev/null 2>&1 && python -c 'import sys' >/dev/null 2>&1; then
	exec python scripts/dg_agent.py "$@"
	fi

	exec python3 scripts/dg_agent.py "$@"