GPT-2 Fine-Tuning Playground 🚀

Welcome to the GPT-2 Fine-Tuning Playground! This repository contains a series of Jupyter notebooks that explore the architecture of GPT-2 and demonstrate how to adapt its behavior for specific tasks, from changing its writing style to imitating a character's persona.

This project is a hands-on journey into the world of transfer learning for language models using the Hugging Face ecosystem.

---

notebooks Overview ✨

This repository is structured as a series of projects, each building on the last.

• 🔬 GPT_2_structure_analysis.ipynb

  • A deep dive into the architecture of the base GPT-2 model. This notebook explores its layers, parameters, and the fundamental components of a transformer-based language model.

• 📰 GPT_2_fine_tuning_news.ipynb

  • Project Act 1: Style Adaptation. This notebook demonstrates how to fine-tune GPT-2 on a news dataset. The goal is to shift the model's default style to a more formal, journalistic tone.

• 🃏 GPT_2_fine_tuning_joker.ipynb

  • Project Act 2: Persona Imitation. A more advanced experiment in stylistic transfer. This notebook fine-tunes GPT-2 on a custom dataset to mimic the chaotic and unpredictable speaking style of the Joker, exploring the challenges and limitations of the process.

---

🛠️ Key Concepts & Technologies

This project covers several key concepts in modern NLP:

• Model Architecture: Understanding the inner workings of GPT-2.
• Fine-Tuning: Adapting a pre-trained model on a custom dataset.
• PEFT (Parameter-Efficient Fine-Tuning): Using LoRA to fine-tune models efficiently.
• Prompt Engineering: Guiding a fine-tuned model to produce desired outputs.
• Libraries: PyTorch, Hugging Face Transformers, PEFT, and Datasets.

---

🏁 Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/your-repository-name.git
   cd your-repository-name

2. Launch Jupyter or Colab Notebook!