How to Set Up Llama 3 Locally on a Mac (Without Ollama)

When it comes to running AI models like Llama 3 locally, there are various methods you can choose from. In my beginner's guide to running Llama 3 with Ollama, I discussed using Ollama to simplify the process. However, for those who want to set up Llama 3 without relying on Ollama or other third-party tools, following the official Meta release instructions is the way to go.

In this post, I'll walk you through the process of setting up Llama 3 on a Mac, using only the official resources from Meta. This gives you more control over the setup and ensures you're working directly with the model in its native environment.

Prerequisites

Before diving into the installation, make sure you have the following:

• A Mac with macOS 12 (Monterey) or higher
  Llama 3 is a large model and may require a powerful machine, so it's recommended to have at least 16GB of RAM, though 32GB or more will significantly improve performance.

• Python 3.10 or later (if using the Python method)
  Ensure Python is installed on your system. If it's not, you can install it using Homebrew or download it from python.org.

• Git
  You will need Git to clone the official Llama 3 repository. Install it using Homebrew with brew install git if it's not already installed.

• PyTorch with Metal support (for M1/M2/M3 Macs)
  If you're using a Mac with an Apple M-chip, you can leverage Metal Performance Shaders (MPS) for hardware acceleration. PyTorch has built-in support for MPS, so make sure your PyTorch installation is compatible with your Mac's hardware. This will significantly improve performance when running Llama 3 locally.

Step-by-Step Guide to Installing Llama 3 on Mac

1. Clone the Official Llama 3 Repository

   First, you'll need to clone the official Llama 3 GitHub repository provided by Meta. Open your terminal and run the following command:

   git clone https://github.com/facebookresearch/llama
   

   This command will download the repository to your local machine.

2. Set Up a Python Virtual Environment

   To avoid dependency issues, it's always a good idea to set up a Python virtual environment. Inside the terminal, navigate to the directory where you cloned the repository:

   cd llama
   

   Then create and activate a virtual environment:

   python3 -m venv llama-env
   source llama-env/bin/activate
   

   With the environment active, any Python libraries you install will be isolated to this project.

3. Install the Required Dependencies

   Now that your virtual environment is active, install the necessary dependencies. These dependencies are listed in the repository's requirements.txt file.

   Run the following command to install them:

   pip install -r requirements.txt
   

   This will ensure that all libraries, such as PyTorch and any specific tooling for Llama 3, are correctly installed.

4. Download Llama 3 Weights

   The Llama 3 model requires access to its pre-trained weights. You'll need to request access to these from Meta, as the model isn't available for download publicly without permissions.

   Follow the instructions in the official Meta release to request access. Once you have the appropriate credentials, you can download the model weights.

   Place the downloaded weights in a directory within the cloned repository.

5. Run Llama 3 Locally

   Once everything is set up, you're ready to run Llama 3 locally on your Mac. Depending on your use case, you can either run it in a standard Python script or interact with it through the command line.

   Running Llama 3 with Python

   Here's an example of how you might initialize and use the model in Python:

   from llama import Llama
   model = Llama.from_pretrained("path/to/llama3-weights")
   
   prompt = "What is the capital of France?"
   response = model.generate(prompt)
   print(response)
   

   This simple script demonstrates how to load the model and run a basic inference task. Replace path/to/llama3-weights with the actual path to the weights on your machine.

   Running Llama 3 from the Command Line

   If you prefer not to use Python, you can also run Llama 3 directly from the command line. If the repository includes a CLI tool, you can use a command like this to run the model:

   ./llama_cli --model path/to/llama3-weights --prompt "What is the capital of France?"
   

   This command runs the model directly from the terminal. Make sure to replace path/to/llama3-weights with the correct path to your downloaded weights.

6. Optimize for Mac's M Chip (Optional)

   If you're running this on an M1/M2/M3 Mac, it's worth taking advantage of Apple's Metal Performance Shaders (MPS) to accelerate computations. PyTorch has built-in support for MPS, so if you're using PyTorch for this project, it should automatically detect your hardware.

   To verify that PyTorch is using MPS, you can run:

   import torch
   print(torch.backends.mps.is_available())
   

   If True is returned, your system is ready to run Llama 3 optimized for Apple Silicon.

Troubleshooting Common Issues

Memory Limitations: If you encounter memory errors, consider running the model with smaller batch sizes or offloading parts of the computation to disk. This is especially important if you are using a Mac with less than 32GB of RAM.

CUDA Errors: Although Mac doesn't officially support CUDA, make sure you're using a compatible version of PyTorch for Apple Silicon. Always check for updates in the Llama repository and PyTorch forums for any platform-specific fixes.

Conclusion

Setting up Llama 3 on a Mac without Ollama is a more hands-on process, but it gives you a deeper understanding of how to work with the model and greater control over its usage. By following this guide, you'll be able to run Llama 3 natively on your Mac, enabling you to harness the power of one of the most advanced AI language models available today.