Improved GPT-3: Nucleus Sampling PyTorch code featured in my latest video #Shorts

In this tutorial, we will be looking at how to implement the Smarter GPT-3: Nucleus Sampling technique using PyTorch code. This technique is used to improve the performance of OpenAI’s GPT-3 model by modifying the way in which text generation is done. By using nucleus sampling, we can generate more diverse and interesting text outputs.

Before we get started, make sure you have PyTorch installed on your system. You can install it using the following command:

pip install torch

Now, let’s start by setting up the environment and importing the necessary libraries:

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load pre-trained GPT-3 model and tokenizer
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Set the device to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

Next, let’s define the nucleus sampling function:

def nucleus_sampling(input_ids, max_length, top_p=0.9):
    # Ensure the model is in evaluation mode
    model.eval()

    with torch.no_grad():
        # Generate text using the input_ids
        output = model.generate(input_ids=input_ids, max_length=max_length, do_sample=True, top_p=top_p)

    # Convert the generated text to a string
    text = tokenizer.decode(output[0], skip_special_tokens=True)

    return text

In this function, we first set the model to evaluation mode and then use the generate method of the GPT-3 model to generate text based on the input ids. We use nucleus sampling with a specified top_p value to control the diversity of the generated text.

Now, let’s test the nucleus sampling function with a simple example:

input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)

generated_text = nucleus_sampling(input_ids, max_length=100, top_p=0.9)
print(generated_text)

In this example, we provide the input text "Once upon a time" to the nucleus_sampling function and set the maximum length of the generated text to 100. The top_p parameter controls the diversity of the generated text.

You can experiment with different input texts and top_p values to see how they affect the output of the nucleus sampling technique.

I hope this tutorial has been helpful in understanding how to implement the Smarter GPT-3: Nucleus Sampling technique using PyTorch code. Feel free to explore further and customize the code to suit your needs. Thank you for reading!