Deployment

📊 Model Deployment Memory and Supported Model Size Reference Guide

Note: "B" in the table represents "billion parameters model". Data shown are examples only; actual supported model sizes may vary depending on system optimization, deployment environment, and other hardware/software conditions.

Memory (GB)	Docker Deployment (Windows/Linux)	Docker Deployment (Mac)	Integrated Setup (Windows/Linux)	Integrated Setup (Mac)
8	~0.8B (example)	~0.4B (example)	~1.0B (example)	~0.6B (example)
16	1.5B (example)	0.5B (example)	~2.0B (example)	~0.8B (example)
32	~2.8B (example)	~1.2B (example)	~3.5B (example)	~1.5B (example)

Note: Models below 0.5B may not provide satisfactory performance for complex tasks. And we're continuously improving cross-platform support - please submit an issue for feedback or compatibility problems on different operating systems.

MLX Acceleration: Mac M-series users can use MLX to run larger models (CLI-only).

🐳 Option 1: Docker Setup

Note: Docker setup on Mac M-Series chips has 25-30% performance overhead compared to integrated setup, but offers easier installation process.

Prerequisites

• Docker and Docker Compose installed on your system

  • For Docker installation: Get Docker

  • For Docker Compose installation: Install Docker Compose

• For Windows Users: You can use MinGW to run make commands. You may need to modify the Makefile by replacing Unix-specific commands with Windows-compatible alternatives.

• Memory Usage Settings (important):

  • Configure these settings in Docker Desktop (macOS) or Docker Desktop (Windows) at: Dashboard -> Settings -> Resources

  • Make sure to allocate sufficient memory resources (at least 8GB recommended)

Setup Steps

1. Clone the repository

git clone [e-mail]:Mindverse/Second-Me.git
cd Second-Me

1. Start the containers

make docker-up

1. After starting the service (either with local setup or Docker), open your browser and visit:

http://localhost:3000

1. View help and more commands

make help

1. For custom Ollama model configuration, please refer to:Custom Model Config(Ollama)

🚀 Option 2: Integrated Setup (Non-Docker)

Note: Integrated Setup provides best performance, especially for larger models, as it runs directly on your host system without containerization overhead.

Prerequisites

• Python 3.12+ installed on your system (using uv)

• Node.js 23+ and npm installed

• Basic build tools (cmake, make, etc.)

Setup Steps

1. Clone the repository

git clone [e-mail]:Mindverse/Second-Me.git
cd Second-Me

1. Setup Python Environment Using uv

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create virtual environment with Python 3.12
uv venv --python 3.12

# Activate the virtual environment
source .venv/bin/activate  # Unix/macOS
# or
# .venv\Scripts\activate  # Windows

1. Install dependencies

make setup

1. Start all services

make restart

1. After services are started, open your browser and visit:

http://localhost:3000

💡 Advantages: This method offers better performance than Docker on Mac & Linux systems while still providing a simple setup process. It installs directly on your host system without containerization overhead. (Windows not tested)