Step-by-Step Guide to Using Together AI with LLaMA 3 and CodeLlama for Python Code Generation

Large language models (LLMs) have revolutionized how we interact with technology, and Together AI brings the power of cutting-edge open-source models like LLaMA 3 and CodeLlama 34B Instruct to your fingertips. In this article, we’ll dive into what makes these models special, how Together AI provides value, and walk you through a practical use case: building a Python application from a natural language description.

Understanding the Building Blocks

What is LLaMA 3?

LLaMA 3 (Large Language Model Meta AI) is an advanced open-source language model developed by Meta. Its strengths include:

• Contextual Understanding: Processes complex natural language prompts with high accuracy.
• Architecture Design: Ideal for generating application plans, architectures, and designs.
• Open-Source: Freely available to developers, enabling flexibility without vendor lock-in.

What is CodeLlama 34B Instruct?

CodeLlama is a specialized version of LLaMA fine-tuned for programming tasks. The “34B” refers to its size in billions of parameters, making it highly capable for:

• Code Generation: Generates Python, JavaScript, and other programming languages.
• Problem Solving: Handles complex programming prompts and adapts to user needs.
• Clear Documentation: Outputs code with detailed comments for easy understanding.

What is Together AI?

Together AI is a platform that simplifies access to leading open-source models. With Together AI, you can:

• Use powerful LLMs like LLaMA 3 and CodeLlama via a single API.
• Avoid proprietary restrictions while leveraging state-of-the-art AI.
• Build applications at a fraction of the cost compared to commercial solutions.

The Use Case: Natural Language to Python Application

Imagine you’re a data analyst or developer who needs to process CSV files. Instead of manually writing the code, what if you could describe the application in plain English and get:

1. A detailed architecture from LLaMA 3.
2. Fully functional Python code generated by CodeLlama.

This pipeline transforms natural language into working software, saving time and effort while promoting collaboration between technical and non-technical users.

Step-by-Step Walkthrough of the Code

Here’s how you can build the natural language-to-code pipeline in Google Colab using Together AI.

1. Install and Set Up Together AI

First, ensure you have Together AI’s library installed and set up your API key.

# Install necessary libraries (uncomment if running for the first time)
# !pip install together

# Import necessary libraries
import os
from together import Together

# Set your Together AI API key
os.environ['TOGETHER_API_KEY'] = 'YOUR_API_KEY_HERE'  # Replace 'YOUR_API_KEY_HERE' with your actual API key

# Initialize Together client
client = Together()

print("Together AI setup complete!")

2. Define the LLaMA 3 Function

LLaMA 3 will take your natural language description and generate a detailed architecture and design for the application.

def get_architecture_with_llama3(description):
    """
    Generate the architecture and design for a Python application based on a natural language description.

    Parameters:
    description (str): A plain-text description of the desired application.

    Returns:
    str: Architecture and design or an error message.
    """
    try:
        prompt = (
            f"You are a software architect. Based on the following natural language description, "
            f"design the architecture and provide a detailed plan for implementing the Python application. "
            f"Focus on the structure, modules, and how the application should work.\n\n"
            f"Description: {description}\n\n"
            f"Architecture and Design:"
        )
        print(f"Generated Prompt for LLaMA 3:\n{prompt}\n")

        # Send request to Together AI
        response = client.chat.completions.create(
            model="meta-llama/Meta-Llama-3-70B-Instruct-Turbo",  # LLaMA 3 model
            messages=[{"role": "user", "content": prompt}]
        )

        # Extract the generated architecture
        architecture = response.choices[0].message.content.strip()
        return architecture

    except Exception as e:
        return f"Error with LLaMA 3: {e}"

3. Define the CodeLlama Function

Once you have the architecture, pass it to CodeLlama to generate Python code.

def generate_code_with_codellama(architecture):
    """
    Generate Python code based on the provided architecture and design using the Meta CodeLlama model.

    Parameters:
    architecture (str): The architecture and design of the application.

    Returns:
    str: Generated Python code or an error message.
    """
    try:
        prompt = (
            f"You are a Python programming assistant. Based on the following architecture and design, "
            f"generate the Python code to implement the application. Ensure the code is clear, well-commented, "
            f"and includes necessary imports.\n\n"
            f"Architecture and Design: {architecture}\n\n"
            f"Generated Python Code:"
        )
        print(f"Generated Prompt for CodeLlama:\n{prompt}\n")

        # Send request to Together AI
        response = client.chat.completions.create(
            model="codellama/CodeLlama-34b-Instruct-hf",  # Meta CodeLlama model
            messages=[{"role": "user", "content": prompt}]
        )

        # Extract the generated code
        generated_code = response.choices[0].message.content.strip()
        return generated_code

    except Exception as e:
        return f"Error with CodeLlama: {e}"

4. Run the Pipeline

Provide a description of your desired application, and the pipeline will generate both the architecture and the code.

# Example usage
# Step 1: User submits a natural language description
description = "I need a Python app that reads a CSV file, filters rows based on a column value, and writes the result to a new CSV file."

# Step 2: Get the architecture and design from LLaMA 3
print("### Architecture and Design Generated by LLaMA 3 ###\n")
architecture = get_architecture_with_llama3(description)
print(architecture)

# Step 3: Use the architecture to generate Python code with CodeLlama
print("\n### Code Generated by Meta CodeLlama 34B Instruct ###\n")
generated_code = generate_code_with_codellama(architecture)
print(generated_code)

Why Together AI is a Game Changer

1. Open-Source Models: Access powerful models without proprietary restrictions.
2. Seamless Integration: Combine models like LLaMA 3 and CodeLlama for innovative applications.
3. Cost-Effective: Build applications without expensive licensing fees.

Conclusion

You’ve just built a powerful pipeline that transforms natural language descriptions into Python code using Together AI. This approach saves time, bridges the gap between technical and non-technical users, and opens up endless possibilities for automation and innovation.

In a future article, we’ll explore how to integrate this pipeline into a user-friendly interface using Gradio, Streamlit, or Dash. Stay tuned! 🚀

Resources

• Together AI Demos
• Together AI Documentation

Let me know if you try this out or have any feedback!

Addendum: Code User Interface with Gradio and CodeLlama for Python Code Generation

# Install necessary libraries (uncomment if running for the first time)
!pip install together gradio

# Import necessary libraries
import os
import gradio as gr
from together import Together

# Set your Together AI API key
os.environ['TOGETHER_API_KEY'] = 'YOUR_API_KEY_HERE'  # Replace with your actual API key

# Initialize Together client
client = Together()

# Function to generate Python code using CodeLlama
def generate_code_with_codellama(description):
    """
    Generate Python code based on a natural language description using CodeLlama.

    Parameters:
    description (str): A plain-text description of the desired Python code.

    Returns:
    str: Generated Python code or an error message.
    """
    try:
        prompt = (
            f"You are a Python programming assistant. Based on the following description, "
            f"generate the Python code. Ensure the code is clear, well-commented, and includes necessary imports.\n\n"
            f"Description: {description}\n\n"
            f"Generated Python Code:"
        )

        # Call Together AI
        response = client.chat.completions.create(
            model="codellama/CodeLlama-34b-Instruct-hf",  # CodeLlama model
            messages=[{"role": "user", "content": prompt}]
        )

        # Extract the generated code
        generated_code = response.choices[0].message.content.strip()
        return generated_code

    except Exception as e:
        return f"Error with CodeLlama: {e}"


# Define Gradio interface function
def gradio_generate_code(description):
    """
    Gradio function to take user input and generate Python code.

    Parameters:
    description (str): User-provided description of the desired Python application or snippet.

    Returns:
    str: Generated Python code.
    """
    if not description.strip():
        return "Please provide a valid description."
    
    # Generate code using CodeLlama
    code = generate_code_with_codellama(description)
    return code


# Create the Gradio interface
interface = gr.Interface(
    fn=gradio_generate_code,
    inputs=gr.Textbox(lines=3, placeholder="Describe the application or code you want", label="Application or Code Description"),
    outputs=gr.Textbox(label="Generated Python Code"),
    title="Python Code Generator with CodeLlama",
    description="Enter a description of the Python application or code you need. The CodeLlama model will generate the corresponding Python code."
)

# Launch the Gradio app
interface.launch(share=True)

How It Works

Input:

• Users enter a description, such as:
• “Write a Python function to calculate the sum of numbers in a list.”
• “Create a script to scrape headlines from a news website using BeautifulSoup.”

Processing:

• The description is passed to CodeLlama via the Together AI API.
• CodeLlama generates Python code based on the description.

Output:

• The generated Python code is displayed in a textbox.

Gradio Interface:

• Runs locally in Colab with a shareable public link.
• Allows users to interact with the app directly in the notebook.

How to Use This Code

Replace API Key:

• Replace 'YOUR_API_KEY_HERE' with your actual Together AI API key.

Run the Notebook:

• Copy the code block into a Colab notebook and run it.

Access the Interface:

• Once the Gradio app launches, you’ll get a shareable link. Use it to interact with the app or share it with others.

Test Use Cases:

• Input natural language descriptions to test the Python code generation.

Example Interaction

Input:

“Write a Python script to count the frequency of words in a text file.”

Output:

from collections import Counter

def word_frequency(file_path):
    """
    Count the frequency of words in a text file.

    Parameters:
    file_path (str): Path to the text file.

    Returns:
    dict: A dictionary with words as keys and their frequencies as values.
    """
    try:
        with open(file_path, 'r') as file:
            text = file.read()
            words = text.split()
            word_counts = Counter(words)
            return dict(word_counts)
    except Exception as e:
        print(f"An error occurred: {e}")

# Example usage
file_path = "example.txt"
print(word_frequency(file_path))

Why Use Gradio?

• Interactive UI: Simplifies the process of testing models.
• Accessible: Provides a shareable link for others to try the application.
• Customizable: Can be expanded to include additional input fields or outputs.

With this setup, you’ve turned a cutting-edge model like CodeLlama into an accessible and interactive Python code generator. 🚀