GPT-OSS Architecture: Implementation

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GPT-OSS: Architecture Implementation

This comprehensive Jupyter notebook provides a hands-on, implementation-focused on exploration of the GPT-OSS (Open Source) model architecture. It builds working components and integrates them into a complete model.

🛠️ Hands-On Implementation

  • Working code for all major GPT-OSS components
  • Live demonstrations showing tensor operations
  • Performance comparisons between architectures
  • Complete model integration with error-free execution
  • Interactive visualizations of attention patterns and expert usage

📋 Table of Contents

  1. 🏗️ Core Architecture Components - Build RMSNorm, RoPE, Attention, MLP blocks
  2. 🔤 Advanced Tokenization - o200k encoding with conversation tokens
  3. 🧠 Model Integration - Complete GPT-OSS vs GPT-2 comparison
  4. 📊 Training & Scaling Analysis - Memory, compute, and performance insights

🚀 GPT-OSS Architecture Highlights

This notebook implements and demonstrates:

  • 🎭 Mixture of Experts (MoE): 8 experts with 2 active per token (4x capacity, 25% compute)
  • 🔍 Grouped Query Attention (GQA): 4:1 query-to-key ratio for memory efficiency
  • 🌀 Rotary Position Embedding (RoPE): Relative positions without learned parameters
  • 📏 RMS Normalization: Simpler, more stable than LayerNorm
  • 🪟 Sliding Window Attention: Efficient long-context processing
  • 🔧 Advanced Tokenization: 200k+ vocabulary with special conversation tokens

✨ Unique Features of This Notebook

✅ Interactive demonstrations - See tensors flow through each component
✅ Error-free integration - Complete working model implementation
✅ Visual comparisons - Charts showing benefits of each innovation
✅ Educational focus - Clear explanations with working example

Let’s dive in and build some cutting-edge AI architecture! 🧠💻

This notebook is for:

  • 📖 Educational demonstrations of GPT-OSS Architecture.

📂 GitHub Repository: GPT-OSS-Architecture