Step-by-Step: Install Llama 3.2 Vision on Mac M1, M2, M3 in Minutes

Llama 3.2 Vision is a collection of advanced, instruction-tuned, multimodal large language models (LLMs) optimized for image reasoning, image captioning, and more. This guide will walk you through the steps to install and use Llama 3.2 Vision on your Mac M1, M2, or M3.

Prerequisites

Before installing Llama 3.2 Vision, make sure Ollama is installed on your Mac. If you haven’t installed it, refer to this guide: Step-by-Step Guide to Installing Ollama on Mac.

Installation Steps

1. Open Terminal

Press Command + Space, type Terminal, and hit Enter to open the Terminal.

2. Install the 11B Version

Run this command to download the 11B model (7.9GB):

ollama run llama3.2-vision

3. Install the 90B Version

To install the 90B model (55GB), use this command:

ollama run llama3.2-vision:90b

4. Verify the Installation

Once the installation is complete, you will see the >>> prompt. This indicates that the model is ready for interaction.

What to Do:

At the >>> prompt, type a question or a command to verify that the model is functioning properly.
Example Interaction:

Describe the contents of this image: /path/to/your/image.jpg.

Supported Languages

For text-only tasks, Llama 3.2 Vision supports: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
For image+text applications, only English is supported.

Use Cases of Llama 3.2 Vision

Here are some practical applications of Llama 3.2 Vision:

1. Image Captioning

Generate descriptive text for images, which is helpful for accessibility and content management.
Example Use: Automatically caption photos for a photography blog.

2. Image Question Answering

Answer questions about images for educational or analytical purposes.
Example Use: Answering “How many people are in this picture?” for an event analysis.

3. Visual Reasoning

Understand and analyze the relationships between objects in images.
Example Use: Determining if there is enough space to park a car in an image.

4. Object Recognition

Identify and classify objects within an image.
Example Use: Detecting various items in a supermarket shelf image for inventory tracking.

5. Scene Understanding

Provide contextual information about a scene, such as whether it’s indoors or outdoors.
Example Use: Identifying a living room setting in a smart home system.

Example Code for Using Llama 3.2 Vision

1. Python Example

import ollama

response = ollama.chat(
    model='llama3.2-vision',
    messages=[{
        'role': 'user',
        'content': 'How many animals are in this image?',
        'images': ['path/to/your/image.jpg']  # Replace with your image path
    }]
)

print(response)

2. JavaScript Example

import ollama from 'ollama';

const response = await ollama.chat({
    model: 'llama3.2-vision',
    messages: [{
        role: 'user',
        content: 'What is the person in this image doing?',
        images: ['path/to/your/image.jpg']  // Replace with your image path
    }]
});

console.log(response);

3. cURL Example

curl http://localhost:11434/api/chat -d '{
    "model": "llama3.2-vision",
    "messages": [
        {
            "role": "user",
            "content": "What is in this image?",
            "images": ["<base64-encoded image data>"]
        }
    ]
}'

Note: For the cURL example, replace "<base64-encoded image data>" with the actual base64-encoded string of your image.

This tutorial should help you get Llama 3.2 Vision up and running on your Mac, and you can start exploring its diverse capabilities across image and text tasks. Happy experimenting!