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 /install_langgraph_local.sh

aogavrilov

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dc7b96e verified 18 days ago

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

	DG_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
	UV_BIN="$DG_ROOT/.tools/uv/bin/uv"
	VENV="$DG_ROOT/.venv-langgraph"
	PYTHON="$VENV/bin/python"
	PYTHON_WIN="$VENV/Scripts/python.exe"
	VENV_WSL="$DG_ROOT/.venv-langgraph-wsl"
	PYTHON_WSL="$VENV_WSL/bin/python"
	WHEELHOUSE="$DG_ROOT/.wheelhouse/langgraph-wsl-cp314"
	UNAME="$(uname -s 2>/dev/null \|\| true)"

	win_to_wsl_path() {
	local value="$1"
	value="${value//\\//}"
	if [[ "$value" =~ ^([A-Za-z]):/(.*)$ ]]; then
	local drive
	drive="$(printf '%s' "${BASH_REMATCH[1]}" \| tr 'A-Z' 'a-z')"
	printf '/mnt/%s/%s' "$drive" "${BASH_REMATCH[2]}"
	else
	printf '%s' "$value"
	fi
	}

	if [[ "$UNAME" != Linux* ]] && command -v wsl.exe >/dev/null 2>&1 && command -v cygpath >/dev/null 2>&1 && [[ -d "$WHEELHOUSE" ]]; then
	export MSYS2_ARG_CONV_EXCL='*'
	export MSYS_NO_PATHCONV=1
	win_root="$(cygpath -am "$DG_ROOT")"
	wsl_root="$(win_to_wsl_path "$win_root")"
	exec wsl.exe bash -lc "cd $(printf '%q' "$wsl_root") && exec ./scripts/install_langgraph_local.sh"
	fi

	if [[ "$UNAME" == Linux* ]]; then
	if [[ -d "$WHEELHOUSE" ]]; then
	if [[ ! -x "$PYTHON_WSL" \|\| "${DG_REFRESH_EXTERNAL_AGENT:-0}" == "1" ]]; then
	python3 -m venv "$VENV_WSL"
	fi
	"$PYTHON_WSL" -m pip install --no-index --find-links "$WHEELHOUSE" "langgraph" "langchain" "langchain-openai"
	PYTHON="$PYTHON_WSL"
	elif [[ ! -x "$UV_BIN" ]]; then
	"$DG_ROOT/scripts/install_uv_local.sh" >/tmp/dg-uv-install.log

	if [[ ! -x "$PYTHON" \|\| "${DG_REFRESH_EXTERNAL_AGENT:-0}" == "1" ]]; then
	"$UV_BIN" venv "$VENV" --python 3.12
	fi

	"$UV_BIN" pip install --python "$PYTHON" "langgraph" "langchain" "langchain-openai"
	fi
	else
	if [[ ! -x "$PYTHON_WIN" \|\| "${DG_REFRESH_EXTERNAL_AGENT:-0}" == "1" ]]; then
	python -m venv "$VENV"
	fi
	"$PYTHON_WIN" -m pip install "langgraph" "langchain" "langchain-openai"
	PYTHON="$PYTHON_WIN"
	fi

	"$PYTHON" - <<'PY'
	from langchain_openai import ChatOpenAI

	try:
	from langchain.agents import create_agent as agent_factory
	except ImportError:
	from langgraph.prebuilt import create_react_agent as agent_factory

	print("langgraph ready")
	print(ChatOpenAI.__name__)
	print(agent_factory.__name__)
	PY