Aider + Llama 3.1: Develop a Full-stack App Without Writing ANY Code!

Just yesterday, Meta AI made waves by dropping Llama 3.1, which is now hailed as the best open-source AI model, rivaling closed-source models like Claude 3.5 Sonic and GPT-4 Omni. It surpasses GPT-3.5 and even GPT-4 in numerous benchmarks. A quick glance at the comparison graph between closed-source and open-weight models reveals that open-source models, particularly Llama 3.1, are on par with or exceed the performance of prominent closed-source models.

Three models are available under Llama 3.1:

405 billion parameter model: This is the flagship foundation model, outpacing every open-source model and matching the performance of many closed-source models.
70 billion parameter model: Known as the cost-effective model.
8 billion parameter model: A lightweight model that can run almost anywhere.

In terms of code generation, Llama 3.1 excels. It's lauded as one of the best open-source models for coding, capable of automating, generating, and debugging code efficiently. Benchmarks under Human Evol and Evol+ show that all three Llama 3.1 models perform similarly or better than GPT-4 Omni and Claude 3.5 Sonic.

Today, we’ll showcase how to pair the new Llama 3.1 model with Aider, an AI pair programmer you can access in your terminal, to create full-stack applications without writing any code. Combining Llama 3.1 with Aider optimizes code generation, debugging, and other development tasks, connecting the best open-source coding-based model with a powerful pair programmer.

Setting Up Llama 3.1 with Aider

Before starting, ensure you have installed:

Ollama (to run the large Llama 3.1 model)
Python and pip
Git

Here's a step-by-step setup guide:

Install Ollama: Run the following command based on your chosen model:
```
ollama run llama 3.1 : <model-parameter-size>
```
For example, to install the 8 billion parameter model:
```
ollama run llama 3.1: 8B
```
This starts downloading the model.
Install Aider: Open your terminal and run:
```
pip install aider-chat
```

Set API Base: Depending on your OS, set the API base:

For Windows:

set OLLAMA_API_BASE=http://localhost:11443

For Linux and Mac:

export OLLAMA_API_BASE=http://localhost:11443

Run Aider with Llama 3.1: Execute the following in your terminal:
```
aider --model llama 3.1: 8B
```
You can start coding immediately by requesting Aider to generate various components. For instance, to create a button:
```
Please create a button.
```
This command generates an HTML button:
```
<button>Click Me</button>
```

Or for something more complex like a sleek, modern SaaS website, you can input:

Can you please generate a sleek and modern website for my SaaS company called World of AI? Make sure there is a pricing plan and more information that is needed for this website.

The result is a basic structure for a modern website.

By pairing Llama 3.1 with Aider, you can transform the way you code, creating high-quality applications without manual coding.

Keywords:

Meta AI
Llama 3.1
Aider
Open-source AI model
Code generation
Full-stack application
Python
Ollama
SaaS website

FAQ:

Q1: What are the parameter sizes available for Llama 3.1? A: Llama 3.1 comes in three parameter sizes: 8 billion, 70 billion, and 405 billion.

Q2: How does Llama 3.1 compare to GPT-4 Omni and Claude 3.5 Sonic? A: Llama 3.1 performs on par with or better than these closed-source models in various benchmarks.

Q3: What are the prerequisites for running Llama 3.1? A: You need to install Ollama, Python, pip, and Git, based on your operating system.

Q4: Can I run the 405 billion parameter model on my local machine? A: Due to its size, it is recommended to run the 405 billion parameter model on a server setup, such as an AWS instance.

Q5: How can I benefit from combining Llama 3.1 with Aider? A: This combination allows for efficient code generation, debugging, and developing full-stack applications without manual coding.

Q6: Where can I find more information on the setup process? A: Links to the tools and detailed instructions are available in the video description provided by the content creator.