🧙‍♂️ GPT for Harry Potter: Generating Magic with Transformers

Skills: Natural Language Processing (NLP), Transformers, GPT, Deep Learning

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🚀 Project Overview

What if we could generate new Harry Potter stories using AI? This project explores the power of transformers and autoregressive models to generate text in the style of J.K. Rowling's Harry Potter series.

Using GPT-style models, this project demonstrates:
✅ Language modeling using character-level and word-level embeddings
✅ Building a transformer-based text generator from scratch
✅ Fine-tuning models to capture the writing style of Harry Potter
✅ Experimenting with different architectures, including the Bigram model

This project is perfect for machine learning and NLP roles, showcasing deep learning expertise in language modeling, sequence generation, and transformer-based architectures.

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🎯 Key Objectives

✔ Understand and implement GPT-style text generation
✔ Experiment with different transformer-based architectures
✔ Analyze and improve model performance with training techniques
✔ Generate coherent, Harry Potter-style text using AI

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📖 Dataset & Preprocessing

This project uses text from Harry Potter books as training data. The text is tokenized into sequences and fed into a model that predicts the next word (or character).

🔹 Tokenization – Converting text into a numerical format
🔹 Vocabulary Creation – Learning word/character embeddings
🔹 Contextual Sequences – Training the model on short sequences

✅ Example: Tokenizing Text for Training

import torch
from transformers import GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

text = "Harry Potter and the Sorcerer's Stone"
tokens = tokenizer.encode(text, return_tensors="pt")

print(tokens)

💡 Why GPT-2?

• Pretrained on large-scale data → Faster training, better context retention
• Autoregressive → Predicts text step-by-step like a real writer

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🔥 Building the Model: GPT for Harry Potter

This project explores multiple text generation models, including:

Model	Description
Bigram Model	Predicts next word based on the last one (simple baseline)
GPT-Style Transformer	Uses attention mechanisms for long-range text coherence
Fine-Tuned GPT-2	Trained on Harry Potter text for better style emulation

✅ Example: Implementing a Simple Bigram Model

import torch.nn as nn

class BigramModel(nn.Module):
    def __init__(self, vocab_size):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, vocab_size)

    def forward(self, x):
        return self.embedding(x)

model = BigramModel(vocab_size=5000)

💡 Key Learning: The Bigram Model produces random, incoherent text because it lacks long-term memory.

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🎭 Experimenting with Transformer-Based Models

🔹 Self-Attention Mechanisms – Helps retain long-range dependencies
🔹 Positional Encodings – Adds order-awareness to sequences
🔹 Training with GPU Acceleration – Uses CUDA for faster processing

✅ Example: Training a Transformer Model

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

input_text = "Hogwarts is a place where"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

output = model.generate(input_ids, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

💡 Why Transformers?
✔ Handles long-range dependencies
✔ Can be fine-tuned for domain-specific text (e.g., Harry Potter books)

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📊 Model Evaluation & Results

Challenges & Key Learnings:

✔ Bigram model generates nonsense because it lacks context
✔ Vanilla GPT-2 generates readable but generic text
✔ Fine-tuned GPT-2 starts capturing the Harry Potter style

✅ Example: Generating a Harry Potter-style Story

input_text = "Harry walked into the Forbidden Forest and"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

output = model.generate(input_ids, max_length=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))

💡 Expected Output:
"Harry walked into the Forbidden Forest and saw a strange glow ahead. He stepped forward cautiously, his wand raised. Suddenly, a voice whispered from the darkness, 'You shouldn't be here, Potter.' Harry turned, his heart pounding..."

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🔮 Future Enhancements

🔹 Train a fine-tuned GPT-2 on all Harry Potter books for better coherence
🔹 Use Reinforcement Learning (RLHF) to improve output quality
🔹 Experiment with GPT-3 or LLaMA for more advanced generation
🔹 Create an interactive chatbot where users can roleplay as wizards

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🎯 Why This Project Stands Out for NLP & ML Roles

✔ Demonstrates deep learning & NLP expertise
✔ Applies cutting-edge transformer models for text generation
✔ Showcases hands-on model fine-tuning and evaluation
✔ Highlights AI’s creative potential for storytelling & entertainment

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🛠 How to Run This Project

1️⃣ Clone the repo:

git clone https://github.com/shrunalisalian/gpt-harry-potter.git

2️⃣ Install dependencies:

pip install -r requirements.txt

3️⃣ Run the Jupyter Notebook:

jupyter notebook HarryPotter.ipynb

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📌 Connect with Me

• LinkedIn: Shrunali Salian
• Portfolio: https://portfolio-shrunali-suresh-salians-projects.vercel.app/
• Email: Your Email

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References: https://www.youtube.com/watch?v=kCc8FmEb1nY&t=3344s