solo-server 0.5.1

Platform for Physiscal AI Inference.

Solo Server

Fastest way to deploy physical AI

CLI for Solo-Server Serving models in the physical world; optimized for on-device operation

Installation

#Choose one of the following for solo-server installation
#1. Install solo server from PyPI python manager
uv pip install solo-server

#2. Install solo server from source
git clone https://github.com/GetSoloTech/solo-server.git
cd solo-server
uv pip install -e .

# Solo commands
solo --help

Video: Solo Tech Installation

Quick Installation for Mac (Automated)

For Mac users, we provide an automated installation script that handles all the setup steps:

# Clone the repository
git clone https://github.com/GetSoloTech/solo-server.git
cd solo-server

# Make the installation script executable
chmod +x install_mac.sh

# Run the automated installation
./install_mac.sh

The script will automatically:

• Install uv package manager (version 0.9.3)
• Create a virtual environment with Python 3.12.12
• Set up environment variables for dependencies
• Install solo-server in development mode with fallback handling for mujoco dependencies

After installation, activate the virtual environment:

source solo_venv/bin/activate

Video: quickstart installation

Solo Commands:

solo --help
                                                                                                           
╭─ Commands ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ setup      Set up Solo Server environment with interactive prompts and saves configuration to config.json.                                                                                                          │
│ robo       Robotics operations: motor setup, calibration, teleoperation, data recording, training, and inference                                                                                                    │
│ serve      Start a model server with the specified model.                                                                                                                                                           │
│ status     Check running models, system status, and configuration.                                                                                                                                                  │
│ list       List all downloaded models available in HuggingFace cache and Ollama.                                                                                                                                    │
│ test       Test if the Solo server is running correctly. Performs an inference test to verify server functionality.                                                                                                 │
│ stop       Stops Solo Server services. You can specify a server type with 'ollama', 'vllm', or 'llama.cpp'                                                                                                          │
│            Otherwise, all Solo services will be stopped.                                                                                                                                                            │
│ download   Downloads a Hugging Face model using the huggingface repo id.                                                                                                                                            │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Start server with SML models

# Note that you will need Docker for solo serve
solo setup
solo serve --server ollama --model llama3.2:1b

Interactive Lerobot With Solo Server

Find more details here: Solo Robo Documentation

# Motors (both) → Calibrate (both) → Teleop
solo robo --type lerobot --motors both
solo robo --type lerobot --calibrate both
solo robo --type lerobot --teleop

# Record a new local dataset with prompts
solo robo --type lerobot --record

# Train Diffusion Policy on a recorded dataset and push to Hub
solo robo --type lerobot --train

# Inference with a hub model id (with optional Teleop override)
solo robo --type lerobot --inference

API Reference

Find more details here: OpenAI -> OpenAI API Docs Ollama -> Ollama API Docs

vLLM & llama.cpp (OpenAI Compatible)

# Chat request endpoint
curl http://localhost:5070/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.2",
    "messages": [{"role": "user", "content": "Analyze sensor data"}],
    "tools": [{"type": "mcp", "name": "VitalSignsMCP"}]
  }'

Ollama

# Chat request endpoint
curl http://localhost:5070/api/chat -d '{
  "model": "llama3.2",
  "messages": [
    {
      "role": "user",
      "content": "why is the sky blue?"
    }
  ]
}'

Configuration

Navigate to config file .solo_server/config.json

{
    "hardware": {
        "use_gpu": false,
        "cpu_model": "Apple M3",
        "cpu_cores": 8,
        "memory_gb": 16.0,
        "gpu_vendor": "None",
        "gpu_model": "None",
        "gpu_memory": 0,
        "compute_backend": "CPU",
        "os": "Darwin"
    },
    "user": {
        "domain": "Software",
        "role": "Full-Stack Developer"
    },
    "server": {
        "type": "ollama",
        "ollama": {
            "default_port": 5070
        }
    },
    "active_model": {
        "server": "ollama",
        "name": "llama3.2:1b",
        "full_model_name": "llama3.2:1b",
        "port": 5070,
        "last_used": "2025-10-09 11:30:06"
    }
}

Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/name)
3. Commit changes (git commit -m 'Add feature')
4. Push to branch (git push origin feature/name)
5. Open Pull Request