Instructions to use OpenTARS/Gargantua-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OpenTARS/Gargantua-Pro 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 OpenTARS/Gargantua-Pro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OpenTARS/Gargantua-Pro:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OpenTARS/Gargantua-Pro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OpenTARS/Gargantua-Pro:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf OpenTARS/Gargantua-Pro:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf OpenTARS/Gargantua-Pro:Q4_K_M

Use Docker

docker model run hf.co/OpenTARS/Gargantua-Pro:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use OpenTARS/Gargantua-Pro with Ollama:
```
ollama run hf.co/OpenTARS/Gargantua-Pro:Q4_K_M
```
Unsloth Desktop

How to use OpenTARS/Gargantua-Pro with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenTARS/Gargantua-Pro:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OpenTARS/Gargantua-Pro:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OpenTARS/Gargantua-Pro with Docker Model Runner:
```
docker model run hf.co/OpenTARS/Gargantua-Pro:Q4_K_M
```

Lemonade

How to use OpenTARS/Gargantua-Pro with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OpenTARS/Gargantua-Pro:Q4_K_M

Run and chat with the model

lemonade run user.Gargantua-Pro-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use OpenTARS/Gargantua-Pro with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenTARS/Gargantua-Pro: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 OpenTARS/Gargantua-Pro:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OpenTARS/Gargantua-Pro with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenTARS/Gargantua-Pro: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 "OpenTARS/Gargantua-Pro: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"

Gargantua Pro (12B)

Gargantua Pro is the big model of the openTARS assistant, built for longer tasks: plan the steps, call the tools, check the result and keep going until the job is done. It uses the same 19 tools as Gargantua 2, on a larger base.

Base: Gemma 4 12B (Apache-2.0), fine-tuned by the openTARS project on its own tool-use data.
File: gargantua-pro-gemma4-12b-Q4_K_M.gguf (7.4 GB, text only).
Runs with: llama.cpp and Ollama (the gemma4 renderer and parser are built in). It needs a graphics card with about 8 GB, or plenty of RAM on the CPU.

Use

printf 'FROM ./gargantua-pro-gemma4-12b-Q4_K_M.gguf\nRENDERER gemma4\nPARSER gemma4\nPARAMETER temperature 0.1\n' > Modelfile
ollama create gargantua-pro -f Modelfile
ollama run gargantua-pro

It calls tools through the standard Ollama tool-calling API; the openTARS app sends the 19 tools for you.

Status

First release. We have not published benchmark numbers for it yet.

Follow the project at project-opentars.github.io/OpenTARS.

Downloads last month: -

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

4-bit

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