Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi

This article provides an in-depth exploration of advanced positional embeddingsAPE, RoPE, and ALiBifor transformer-based models like GPT, emphasizing their mathematical foundations, intuitive understanding, and practical implementation in PyTorch. Through detailed explanations and experiments on the TinyStories dataset, it demonstrates how these embeddings enhance the model's ability to capture positional information, leading to improved performance and efficiency in natural language processing tasks.