Introducing the Llama3 Package: Seamlessly Interact with Meta’s Llama 3 Model Locally

2 min readMay 22, 2024

Please note: I built this while working for REBL.ai, for ease of use within the REBL ecosystem.

Join the platform here -> REBL

Welcome to the cutting edge of AI development with Llama3, the first Python package of its kind that enables seamless interaction with Meta’s Llama 3 model on your local machine. Say goodbye to complex setups and hello to streamlined AI integration! The Llama3 python package takes care of everything, from installation to configuration, so you can focus on what truly matters: building innovative projects with the power of Llama 3.

Why Llama3?

Llama3 is designed for developers who want to leverage the incredible capabilities of Meta’s Llama 3 model without the hassle of manual setup. With Llama3, you can:

Automatically install and configure Ollama and Llama 3.
Seamlessly run the model on your local machine.
Focus on development rather than infrastructure.

Getting Started

Ready to dive in? Here’s how to get started with Llama3 in just a few easy steps.

Installation

Step 1: Create a Virtual Environment

First, create a virtual environment to manage your dependencies:

python3 -m venv myenv
source myenv/bin/activate  # On Windows use `myenv\Scripts\activate`

Step 2: Install the Llama3 Package

To begin, you need to install the Llama3 package. You can do this easily using pip:

pip install llama3_package

Usage

Once you have installed the Llama3 package, you can start using it immediately. The package takes care of starting the Ollama server, pulling the Llama 3 model, and running it. You can interact with the model using the `Llama3Model` class provided by the package.

Example

Here’s a quick example to get you started:

from llama3 import Llama3Model

# Initialize the model
model = Llama3Model()

# Send a prompt to the model
response = model.prompt("What is 2 sqared? and what if you multiplied it by 4?")
print("Prompt Response:", response)

# Stream a prompt to the model
for chunk in model.stream_prompt("Tell me a joke"):
print("Stream Prompt Response:", chunk)

In this example, we initialize the Llama3 model, send a simple arithmetic prompt to get a response, and stream a prompt to receive a joke in chunks.

Configuration

You can configure the model using environment variables. For example, to use a different version of the Llama 3 model, you can set the `LLAMA3_MODEL_NAME` environment variable:

export LLAMA3_MODEL_NAME="llama3–70B"

Troubleshooting

If you encounter any issues while using the Llama3 package, here are a few things to check:

Internet Connection: Ensure you have an active internet connection for downloading and pulling the model.
System Requirements: Verify that your system meets the requirements for running Ollama.