Host Open-Source LLMs Locally and Securely from Hugging Face with Docker
Felipe FerreiraUncategorized






A Step-by-Step Guide for Beginners

Leverage the power of open-source Large Language Models (LLMs) by hosting them on your own machine. This guide simplifies the process using Hugging Face models, Docker, and text-generation-webui, ensuring secure LAN access for your AI projects and automation.

Getting Started: Setting Up Your Local LLM Environment

Welcome! We’ll set up your own Large Language Model (LLM) environment. Hugging Face provides open-source AI models, like a central library for many LLMs. Running LLMs locally offers privacy, full control, and offline access.

Docker is crucial for this. It packages software into isolated containers, simplifying setup and dependency management. We use Docker Desktop for easy installation on Windows and macOS.

Before starting, ensure your system has at least 16GB RAM for smaller models; 32GB+ is better for larger ones. A dedicated GPU (NVIDIA preferred) is highly recommended for performance, though not strictly required for initial setup.

We’ll use text-generation-webui as our primary interface. It’s user-friendly, allowing you to interact with your local LLMs without deep technical knowledge. Just like a simple web browser lets you access complex websites.

Installation, Configuration, and Secure LAN Access

Assumptions: git and Docker are installed.

  1. Clone the repository: 
    git clone https://github.com/oobabooga/text-generation-webui.git
cd text-generation-webui
  2. Download an LLM: Choose a model from Hugging Face (e.g., “TheBloke/Mistral-7B-OpenOrca-GGUF”). Download the .gguf file (e.g., mistral-7b-openorca.Q4_K_M.gguf) and place it in the newly created text-generation-webui/models/ directory.
  3. Launch with Docker: 
    docker run --rm -it -p 7860:7860 -v $(pwd)/models:/text-generation-webui/models -v $(pwd)/characters:/text-generation-webui/characters oobabooga/text-generation-webui python server.py --model mistral-7b-openorca.Q4_K_M.gguf --listen

    The --listen flag makes the UI accessible on your LAN.

  4. Verification: Open a web browser on a LAN device and navigate to http://<your_server_ip>:7860.

Common failure: Permissions issues with model downloads. Fix: Ensure the models directory has correct write permissions for the Docker user. For security, enable your operating system’s firewall and use a strong Wi-Fi password. This setup provides a powerful local AI accessible across your network.


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