In this tutorial, we're going to explore how to train CodeLLMA using strong supervision from GPT-4 and other LLMs. This approach, known as the LLM-as-judge method, offers a powerful way to align language models with specific preferences or goals.
Before we dive into the specifics of training CodeLLMA, let's start with a simple example to ensure our environment is set up correctly. Here's a basic "Hello, World!" program in Python:
# This is a simple Python script
print("Hello, World!")
# Let's define a function
def greet(name):
return f"Hello, {name}!"
# Using the function
result = greet("CodeLLMA")
print(result)
This simple code serves as a starting point for our more complex training process. It's a good practice to test your environment with basic commands before proceeding with more advanced operations.
The LLM-as-judge approach involves using a more advanced language model (in this case, GPT-4) to evaluate and guide the training of another model (CodeLLMA). This method allows us to leverage the capabilities of a highly sophisticated model to improve the performance and alignment of our target model.
As we progress through this tutorial, we'll provide more detailed code examples and explanations for each step of the process. The goal is to give you a comprehensive understanding of how to implement preference alignment using the LLM-as-judge approach with CodeLLMA and GPT-4.