Setting up Meta’s AI models on a personal or production server can unlock powerful machine learning capabilities. Whether you're deploying the popular PyTorch framework or experimenting with Meta’s LLaMA (Large Language Model Meta AI) models, the installation process involves several key steps. This guide walks you through setting up the environment, installing dependencies, and running Meta’s AI tools effectively.

1. Preparing Your Server

Before installing anything, ensure your server meets the hardware requirements:

  • At least 16GB RAM (32GB+ recommended for large models)

  • NVIDIA GPU with CUDA support (if you plan to leverage GPU acceleration)

  • Ubuntu 20.04+ or a similar Linux distribution

Update your packages:

sudo apt update && sudo apt upgrade -y

Install essential tools:

sudo apt install build-essential git curl wget python3 python3-pip -y

2. Installing CUDA and cuDNN (Optional for GPU)

If using a GPU, download and install the appropriate CUDA and cuDNN versions compatible with PyTorch:

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-repo-ubuntu2004_11.8.0-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2004_11.8.0-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub
sudo apt update
sudo apt install cuda -y

Add CUDA to your path:

echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

3. Installing PyTorch

Meta’s AI tools rely heavily on PyTorch. Install it with CUDA support (or CPU-only if no GPU):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

For CPU-only:

pip install torch torchvision torchaudio

4. Cloning and Installing Meta’s AI Repositories

For example, to work with LLaMA models:

git clone https://github.com/facebookresearch/llama.git
cd llama
pip install -r requirements.txt

Make sure any additional dependencies are installed as per repository instructions.

5. Downloading Model Weights

Due to licensing restrictions, Meta requires filling out a request form to access LLaMA model weights. Once approved, download the weights and place them in the appropriate directory inside the cloned repository.

You may need to convert weights to a compatible format or set environment variables pointing to them, depending on the tool.

6. Running Inference

Once installed, you can run inference scripts or train fine-tuned models. For example:

python3 generate.py --model-path ./models/llama-7b --prompt "Hello, world!"

Refer to the repository’s documentation for available flags and tuning parameters.

Final Thoughts

Installing Meta’s AI systems requires careful attention to dependencies, hardware requirements, and licensing agreements for proprietary model weights. With the right setup, your server can run state-of-the-art AI models from Meta, enabling applications ranging from natural language processing to computer vision.