So basically, these models are designed to learn how to write code by analyzing textbook quality data from the web and synthetically generated exercises with GPT-3.5. They’re called “Transformer” because they use a fancy algorithm that can process large amounts of information at once (like a transformer machine in a factory).

The decoder part is what actually generates the code, but it needs some guidance from the input text to know where to start and how to proceed. That’s why we feed it “textbook quality data” this helps it understand the basics of programming concepts like loops, functions, and variables.

The GPT-3.5 part is what generates the exercises for Phi-1 and Phi-1.5 to practice on. It’s basically a super smart AI that can write code just as well (if not better) than humans in some cases. But since it doesn’t have any real-world experience, we need to train our models with its output to make sure they can handle more complex coding scenarios.

Now let me explain how the decoder part actually works. It takes a sequence of input tokens (like words or symbols) and uses attention mechanisms to focus on specific parts of that sequence based on their importance for generating the output code. This helps it avoid getting distracted by irrelevant information and stay focused on what’s really important.

For example, let’s say we want our model to generate a function that calculates the area of a rectangle given its length and width. The input text might look something like this: “To calculate the area of a rectangle, multiply the length by the width.” Our decoder would then focus on the words “length” and “width” because they’re crucial for generating the output code (which involves using those variables in our function).

But what if we want to generate a more complex program that uses loops or conditional statements? That’s where Phi-1.5 comes in it has a larger size than Phi-1 and can handle more advanced coding scenarios thanks to its improved architecture. And since both models are trained on the same input data, they should be able to generate code with similar accuracy (although Phi-1.5 might take longer to train due to its increased complexity).

5. It’s not exactly rocket science, but it does involve some fancy algorithms that can help us write code more efficiently (and with fewer errors).