Meta AI's new Llama-3.1 model is a game-changer in the world of open-source large language models. With impressive benchmarks that rival closed-source models, this 405 billion parameter model can be your personal coding copilot.

By integrating Llama-3.1 with the powerful Continued Dev tool, you can unlock a new level of coding productivity and efficiency. Continued Dev leverages advanced models to provide real-time suggestions, error detection, and optimization tips, all powered by the Llama-3.1 model.

To get started, you'll need to set up the prerequisites, including installing Visual Studio Code and the Continued Dev extension. Then, you can choose to either use the 405 billion parameter model through the Together AI API or opt for the 8 billion or 70 billion parameter models installed locally.

Once you've set up the integration, you can start chatting with the Llama-3.1 model within Continued Dev, tapping into its impressive coding capabilities. As a demonstration, we've generated a basic snake game, showcasing the model's ability to quickly and efficiently create functional code.

With Llama-3.1 as your personal coding copilot, you can elevate your coding skills, improve code quality, and boost productivity. Explore the full capabilities of Continued Dev and Llama-3.1 by checking out the additional resources provided in the description.

To integrate the Llama-3.1 model with ContinueDev, follow these steps:

1. Install Visual Studio Code and the ContinueDev extension.
2. Decide which Llama-3.1 model you want to use:
   • 405 billion parameter model: Integrate with the Together AI API.
   • 70 billion or 8 billion parameter model: Install locally using the Hugging Face Transformers library.
3. If using the 405 billion parameter model:
   • Create a Together AI account and obtain the API key.
   • In VS Code, install the Together AI extension and enter the API key.
   • Select the "Llama 3 Model" and configure the title and model settings.
4. If using the local models:
   • Install the Hugging Face Transformers library and download the desired Llama-3.1 model.
   • In the ContinueDev extension, select the installed Llama-3.1 model.
5. Start using the Llama-3.1 model within ContinueDev for real-time coding assistance, error detection, and optimization tips.

By integrating the powerful Llama-3.1 model with ContinueDev, you can enhance your coding productivity and create applications with ease, even without the need for GPUs.

To utilize the powerful Llama-3.1 model, you have two options:

1. Run Llama-3.1 Locally:

   • Install the Llama-3.1 model locally using the provided commands for the 8 billion or 70 billion parameter models.
   • Set up the Llama-3.1 integration within the Codex extension in Visual Studio Code.
   • Start chatting with the locally installed Llama-3.1 model through the Codex extension.

2. Leverage the Power of Together AI:

   • Create an account with Together AI to access their free tier and obtain an API key.
   • Integrate the Together AI API key within the Codex extension in Visual Studio Code.
   • Select the "Meta Llama 3.1 45 Billion Instruct Turbo" model to leverage the 405 billion parameter Llama-3.1 model.
   • Test the capabilities of the Llama-3.1 model by generating a simple Snake game within seconds.

Regardless of the approach, you can now utilize the impressive Llama-3.1 model as your personal coding co-pilot, benefiting from its advanced coding capabilities and seamless integration with Codex and Visual Studio Code.

To test the integration of the Llama-3.1 model with the Continued Dev tool, we will create a simple snake game using the code generated by the model.

First, we will create a new Python file in Visual Studio Code. Then, we will copy and paste the following code into the file:

import curses
from curses import KEY_RIGHT, KEY_LEFT, KEY_UP, KEY_DOWN
from random import randint

# Set up the game window
screen = curses.initscr()
curses.curs_set(0)
screen_height, screen_width = screen.getmaxyx()
window = curses.newwin(screen_height, screen_width, 0, 0)
window.keypad(1)
window.timeout(100)

# Initialize the snake and food
snake = [(4, 4), (4, 3), (4, 2)]
food = (randint(1, screen_height - 2), randint(1, screen_width - 2))

# Game loop
while True:
    next_key = window.getch()
    key = next_key if next_key != -1 else KEY_DOWN

    if key == KEY_DOWN:
        new_head = (snake[0][0] + 1, snake[0][1])
    if key == KEY_UP:
        new_head = (snake[0][0] - 1, snake[0][1])
    if key == KEY_LEFT:
        new_head = (snake[0][0], snake[0][1] - 1)
    if key == KEY_RIGHT:
        new_head = (snake[0][0], snake[0][1] + 1)

    snake.insert(0, new_head)

    if new_head == food:
        food = (randint(1, screen_height - 2), randint(1, screen_width - 2))
    else:
        snake.pop()

    window.clear()
    window.border(0)
    for segment in snake:
        window.addch(segment[0], segment[1], '#')
    window.addch(food[0], food[1], '@')
    window.refresh()

Save the file to your desktop, and then run the code. You should see a simple snake game appear in the Visual Studio Code terminal.

This demonstrates the capability of the Llama-3.1 model to generate functional code, which can then be integrated with the Continued Dev tool to enhance your coding productivity and efficiency.

The integration of the powerful Llama 3.1 model, specifically the 405 billion parameter version, with the Continued Dev AI-powered tool is a game-changer for developers. This open-source large language model, which outperforms many closed-source counterparts, can now be seamlessly integrated into your coding workflow, providing real-time suggestions, error detection, and optimization tips.

By leveraging the Together AI API, you can access this impressive model without the need for resource-intensive local setup. The step-by-step guide provided in this video ensures a smooth integration process, allowing you to harness the full potential of this cutting-edge technology.

Whether you're a seasoned developer or just starting your coding journey, this integration can significantly enhance your productivity and code quality. The ability to generate a functional snake game within seconds is a testament to the power of this AI-driven coding assistant.

To further explore the capabilities of Continued Dev, be sure to check out the additional video resources mentioned in the transcript. Dive deeper into the features and discover how you can optimize your coding workflow with this powerful tool.

Remember, the world of AI is rapidly evolving, and staying up-to-date with the latest advancements is crucial. Follow the author on Patreon and Twitter to ensure you don't miss any exciting updates in the AI landscape.