Instructions to use Ratnesh123/jarvis-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Ratnesh123/jarvis-router 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 Ratnesh123/jarvis-router:Q8_0
# Run inference directly in the terminal:
llama cli -hf Ratnesh123/jarvis-router:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ratnesh123/jarvis-router:Q8_0
# Run inference directly in the terminal:
llama cli -hf Ratnesh123/jarvis-router:Q8_0

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 Ratnesh123/jarvis-router:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Ratnesh123/jarvis-router:Q8_0

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 Ratnesh123/jarvis-router:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ratnesh123/jarvis-router:Q8_0

Use Docker

docker model run hf.co/Ratnesh123/jarvis-router:Q8_0

LM Studio
Jan
Ollama
How to use Ratnesh123/jarvis-router with Ollama:
```
ollama run hf.co/Ratnesh123/jarvis-router:Q8_0
```
Unsloth Desktop

How to use Ratnesh123/jarvis-router with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ratnesh123/jarvis-router:Q8_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": "Ratnesh123/jarvis-router:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Ratnesh123/jarvis-router with Docker Model Runner:
```
docker model run hf.co/Ratnesh123/jarvis-router:Q8_0
```

Lemonade

How to use Ratnesh123/jarvis-router with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ratnesh123/jarvis-router:Q8_0

Run and chat with the model

lemonade run user.jarvis-router-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Ratnesh123/jarvis-router with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ratnesh123/jarvis-router:Q8_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 Ratnesh123/jarvis-router:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Ratnesh123/jarvis-router with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ratnesh123/jarvis-router:Q8_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 "Ratnesh123/jarvis-router:Q8_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"

Jarvis router (FunctionGemma-270M + LoRA, q8_0 GGUF)

Tier-1 intent router for the on-device Jarvis assistant (github.com/ratneshdagli/OnDeviceAI). Maps a short spoken command to one of 15 phone tools (call, message, timer, alarm, reminder, navigate, ...) or escalate(). The app only acts on a call when the token confidence is high and every argument is grounded in the utterance.

File: jarvis-router-q8_0.gguf (~285 MB), runs with llama.cpp.
Base: FunctionGemma-270M, fine-tuned with LoRA, merged, quantised to q8_0.
Prompt format and tool list: packages/agent_core/lib/src/agents/router.dart in the repo.
Licence: derived from Gemma, so the Gemma Terms of Use apply.

Downloads last month: 32

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

8-bit

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