Instructions to use OpenTARS/Gargantua-2 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-2 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-2:Q4_0
# Run inference directly in the terminal:
llama cli -hf OpenTARS/Gargantua-2:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OpenTARS/Gargantua-2:Q4_0
# Run inference directly in the terminal:
llama cli -hf OpenTARS/Gargantua-2:Q4_0

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-2:Q4_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf OpenTARS/Gargantua-2:Q4_0

Use Docker

docker model run hf.co/OpenTARS/Gargantua-2:Q4_0

Llama new
LM Studio
Jan
Ollama
How to use OpenTARS/Gargantua-2 with Ollama:
```
ollama run hf.co/OpenTARS/Gargantua-2:Q4_0
```
Unsloth Desktop

How to use OpenTARS/Gargantua-2 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-2:Q4_0

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-2:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OpenTARS/Gargantua-2 with Docker Model Runner:
```
docker model run hf.co/OpenTARS/Gargantua-2:Q4_0
```

Lemonade

How to use OpenTARS/Gargantua-2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OpenTARS/Gargantua-2:Q4_0

Run and chat with the model

lemonade run user.Gargantua-2-Q4_0

List all available models

lemonade list

Hermes Agent

How to use OpenTARS/Gargantua-2 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-2:Q4_0

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-2:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OpenTARS/Gargantua-2 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-2:Q4_0

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-2:Q4_0" \
  --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 2 (2B)

Gargantua 2 is the main model of the openTARS assistant. It runs locally, opens programs, types, clicks, searches the web and runs commands, and it can look at a screenshot.

Base: Qwen3.5 2B (Apache-2.0), fine-tuned by the openTARS project on its own tool-use, text-action and screen data, including code examples.
Files: gargantua-1.2-qwen3.5-2b-Q4_0.gguf (1.2 GB) and the vision projector mmproj-gargantua-qwen3.5-2b-Q8_0.gguf (0.36 GB).
Runs with: llama.cpp (llama-server), and with Ollama (ProjectOpenTARS/Gargantua-2:latest).

Measured results (openTARS test suites, local machine)

These are our own tests, run with the openTARS app on an RTX 3050 6 GB. They are not public benchmarks.

Test	Result
80-task Lab suite (tools, text, vision, refusals)	73/80
Portable test, 48 tasks (`opentars --prova`)	46/48
Text actions (rewrite, summarize, translate, ask about text)	71/76
Tool-call choice (21 cases)	21/21
100-task suite with side-effect check (`--cem`, 1 Nov 2026)	94/100

Known limits: it can still invent facts about things it does not know (the app adds a warning and web checks for those cases), it is weak at reading small text in screenshots, and it can answer a greeting with a web search.

Use

With llama.cpp:

llama-server -m gargantua-1.2-qwen3.5-2b-Q4_0.gguf --mmproj mmproj-gargantua-qwen3.5-2b-Q8_0.gguf -c 8192

With Ollama:

ollama run ProjectOpenTARS/Gargantua-2:latest

Recommended sampling: temperature 0.4, top_p 0.8, top_k 20, repeat_penalty 1.05. The openTARS app sends think=false for this model.

Checksums

See SHA256SUMS. The GGUF file's SHA-256 matches the layer already published on Ollama (99b7dd09…).

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

4-bit

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Model tree for OpenTARS/Gargantua-2

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Qwen/Qwen3.5-2B-Base

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