Unlock the Power of Llama3 8B Model with Apple MLX Server and Chainlit

Introduction

In this article, we will explore how to set up an Apple MLX Server and download the Llama3 8 billion param model using simple commands. We will then create a user-friendly UI to interact with the model using Chainlit. This combination will enable us to harness the power of large language models and build innovative applications.

Why do we need this setup?

1. Large Language Models: LLMs like Llama3 have revolutionized the field of natural language processing. They can generate human-like text, answer questions, and perform various tasks. However, they require significant computational resources and expertise to set up and interact with.
2. User-Friendly Interface: Chainlit provides a simple and intuitive way to interact with LLMs, making it easier for developers to build applications without requiring extensive knowledge of the underlying model.
3. Apple MLX Server: The Apple MLX Server provides a scalable and secure environment for deploying and managing LLMs, making it an ideal choice for building production-ready applications.

What are the components?

1. Apple MLX Server: Using Apple's MLX framework - deploying a server and deploying Llama3 with it.
2. Llama3 8 billion parameter model: A pre-trained language model with 8 billion parameters, capable of generating human-like text and performing various NLP tasks.
3. Chainlit: A library for building user-friendly interfaces to interact with large language models.
4. AsyncOpenAI: A library for interacting with OpenAI's API, used for generating text completions.

Step 1: Install Dependencies

To get started, we need to install the necessary dependencies. Run the following commands:

pip install mlx-lm openai chainlit        

Step 2: Install the Model and Create the Server

Next, we need to install the Llama3 8 billion param model and create the Apple MLX Server. (The model is ~5GB)

Run the following command:

python -m mlx_lm.server --model mlx-community/Meta-Llama-3-8B-Instruct-4bit --log-level DEBUG        

Step 3: Create the UI with Chainlit

Create a new file named main.py and paste the following code:

Python

from openai import AsyncOpenAI
import chainlit as cl

client = AsyncOpenAI(base_url="https://localhost:8080/v1", api_key="fake-key")
cl.instrument_openai()
settings = {
    "model": "llama3-8b",
    "temperature": 0,
}

@cl.on_message
async def on_message(message: cl.Message):
    response = await client.chat.completions.create(
        messages=[
            {
                "content": "You are a helpful bot, you reply includes Emojis",
                "role": "system"
            },
            {
                "content": message.content,
                "role": "user"
            }
        ],
        **settings
    )
    await cl.Message(content=response.choices[0].message.content).send()        

Step 4: Run the App

Finally, run the app using the following command:

chainlit run main.py        

A browser running the chainlit should appear with a text input block to interact with our served Llama3 model.

Conclusion

In this article, we have demonstrated how to set up an Apple MLX Server and download the Llama3 8 billion param model. We have also created a user-friendly UI to interact with the model using Chainlit. This combination has the potential to revolutionize the way we build language-based applications. Try it out and explore the possibilities!