🚀 GPT-2 Transformer Implementation and Mechanistic Interpretability

This repository contains a comprehensive implementation and exploration of transformer-based language models, with a focus on GPT-2 architecture and mechanistic interpretability. The project demonstrates a deep understanding of modern NLP techniques and transformer architecture internals.

📝 Repository Structure

The repository is organized into several key components:

• cleanGPT: A clean, well-documented implementation of GPT-2 from scratch
• mechanistic_interpretability: Tools and experiments for understanding how transformers work internally
• nanoGPT: A lightweight implementation of GPT-2 with training and evaluation scripts

✨ Key Features

🧠 Clean GPT-2 Implementation

The cleanGPT directory contains a detailed, educational implementation of GPT-2 with:

• Comprehensive tokenization explanation and implementation
• Step-by-step transformer architecture building blocks:
  • Token and positional embeddings
  • Multi-head self-attention mechanism
  • Feed-forward networks
  • Layer normalization
• Training pipeline with optimization techniques
• Text generation capabilities with various sampling methods (greedy, top-k, top-p)
• Advanced features like beam search and key-value caching for efficient inference

🔬 Mechanistic Interpretability

The mechanistic_interpretability directory showcases advanced techniques for understanding transformer internals:

• Induction Head Analysis: Implementation and visualization of attention patterns
• Trigram Detection: Experiments with models trained to detect specific token patterns
• Superposition Analysis: Exploration of how models represent more features than dimensions
• Visualization Tools: Custom plotting utilities for attention patterns and feature representations

⚡ NanoGPT Implementation

The nanoGPT directory provides a production-focused implementation with:

• Efficient transformer blocks with detailed comments explaining each component
• Training pipeline with learning rate scheduling
• Data processing for large-scale datasets (FineWeb)
• Evaluation on benchmark datasets (HellaSwag)

🛠️ Technical Skills Demonstrated

This repository showcases expertise in:

• Deep Learning Frameworks: PyTorch, Transformer-Lens
• Natural Language Processing: Tokenization, language modeling, text generation
• Model Architecture: Transformer design, attention mechanisms, residual connections
• Optimization Techniques: Learning rate scheduling, weight decay, AdamW
• Interpretability Methods: Attention visualization, feature attribution, circuit analysis
• Software Engineering: Clean code organization, type annotations, efficient implementations
• Mathematics: Linear algebra, probability, information theory

🎓 Applications

The implementations in this repository can be used for:

1. Educational Purposes: Understanding transformer architecture from first principles
2. Research: Exploring model behavior and interpretability
3. Production: Building and fine-tuning language models for specific applications
4. Experimentation: Testing hypotheses about how language models work

🚀 Getting Started

To explore this repository:

1. Start with the cleanGPT/cleangpt2.py file for a comprehensive introduction to transformer architecture
2. Explore the mechanistic interpretability notebooks to understand how transformers process information
3. Check out the nanoGPT implementation for a more production-ready approach

📦 Dependencies

• PyTorch
• Transformer-Lens
• Einops
• NumPy
• Matplotlib/Plotly
• Tiktoken
• Datasets

📚 References

• ARENA Chapter 1: Transformer Interpretability - https://arena-chapter1-transformer-interp.streamlit.app/
• Attention Is All You Need - https://arxiv.org/pdf/1706.03762
• Language Models are Unsupervised Multitask Learners (GPT-2) - https://arxiv.org/pdf/2005.14165
• Language Models are Few-Shot Learners (GPT-3) - https://arxiv.org/pdf/2005.14165
• What is a Transformer? (Transformer Walkthrough Part 1/2) - https://youtu.be/bOYE6E8JrtU?si=aZ2KFIXRjOyxWr52
• A Mathematical Framework for Transformer Circuits - https://transformer-circuits.pub/2021/framework/index.html
• An Analogy for Understanding Transformers - https://www.lesswrong.com/posts/euam65XjigaCJQkcN/an-analogy-for-understanding-transformers
• Induction heads - illustrated - https://www.lesswrong.com/posts/TvrfY4c9eaGLeyDkE/induction-heads-illustrated
• Transformer Feed-Forward Layers Are Key-Value Memories - https://arxiv.org/pdf/2012.14913
• Toy Models of Superposition - https://transformer-circuits.pub/2022/toy_model/index.html