GPT-2 Code Golf

About

This skill provides specialized guidance for implementing lightweight GPT-2 inference from scratch, specifically tailored for code golf and resource-constrained environments. It covers the technical intricacies of parsing TensorFlow binary checkpoints, implementing byte-pair encoding (BPE) tokenization without external libraries, and executing efficient forward passes while maintaining numerical stability. By emphasizing a 'verify-before-optimize' workflow, it helps developers navigate common pitfalls like endianness issues, incorrect merge orders, and overflow errors in softmax layers to ensure a functional yet compact solution.

Key Features

• Minimalist BPE tokenization implementation strategies
• 16 GitHub stars
• Identification of common ML implementation anti-patterns
• Binary checkpoint parsing guidance for TensorFlow formats
• Incremental verification and testing workflows
• Numerical stability techniques for transformer forward passes

Use Cases

• Participating in code golf challenges involving machine learning models
• Implementing neural network inference from scratch in low-level languages
• Deploying GPT-style models in environments with extreme size constraints