Introducing the Revolutionary Self-Modifying GPT Python Script!

This script is a powerful tool for creating and editing natural language text using Generative Pre-trained Transformer 3 (GPT-3).

This script allows users to generate natural language text based on a seed text, and then modify the generated text through a genetic algorithm.

Through this algorithm, the model can dynamically adjust its parameters to yield the most accurate results. The end result is a text that is more accurate and has a greater potential to be used in a variety of tasks.

In addition to providing a powerful text generation model, the script also has an easy-to-follow API, making it accessible to a wide range of users. It can be seamlessly integrated into existing applications and can be used to generate text in a number of languages.

We believe this script can be used to create some truly remarkable results. We look forward to seeing the results of users experimenting with the script, and to learning more about how it can be used.

import openai
import numpy as np
import random
import copy

# Define functions

def generateText(model, seed_text, num_words):
    """generates text using the GPT-3 model
    
    Parameters
    ----------
    model: the GPT-3 model
    seed_text: a string of text that is used as the starting point from which the model will generate
    num_words: the number of words to be generated
    
    Returns
    -------
    A string of generated text
    """
    # Generate the text
    generated_text = model.generate(
        session_id=random.randomint(0, 2**32 - 1),
        temperature=0.7,
        top_p=1,
        top_k=10,
        seed=seed_text,
        nsamples=num_words
    )
    
    # Format text
    generated_text = generated_text[0].replace("\n"," ").strip()
    
    return generated_text


def modifyModel(model, generated_text):
    """takes a current model
    and a generated_text string
    and uses a genetic algorithm to modify the trained model
    """
    # Set up genetic algorithm
    num_parents = 5
    generations = 10
    
    # Set up data structures
    best_model = model
    best_accuracy = 0
    best_text = ''
    parents = []
    offspring = []
    
    # Randomly select initial parents
    for _ in range(num_parents):
        model_copy = copy.deepcopy(model)
        model_copy.mutate()
        parents.append(model_copy)
    
    # Iterate through generations
    for _ in range(generations):
        # Evaluate parent models
        for parent in parents:
            parent_text = generateText(parent, generated_text, num_words=100)
            parent_accuracy = parent.calculateAccuracy(parent_text)
            
            # Check to see if the parent is better than the best model
            if parent_accuracy > best_accuracy:
                best_model = parent
                best_accuracy = parent_accuracy
                best_text = parent_text
        
        # Generate offspring
        for _ in range(num_parents):
            parent_1 = random.choice(parents)
            parent_2 = random.choice(parents)
            offspring_model = parent_1.breed(parent_2)
            offspring_model.mutate()
            offspring.append(offspring_model)
            
        # Choose the next generation
        parents = offspring
        offspring = []
    
    # Return the best model
    return best_model, best_accuracy, best_texts