A generative pretrained transformer implementation This project implements an autoregressive sequence model using a transformer architecture. The model processes sequences of bytes (8-bit tokens), learning to predict the next byte given previous context. While this implementation trains on text data, the architecture is agnostic to the content. It can model any byte stream, including, but not limited to, DNA/RNA sequences, compressed data, images, audio, video, or executable binaries. The architecture begins with a token embedding layer that converts each byte into a continuous vector representation. The core of the model is a multi-layer transformer that processes the embedded sequences. Each transformer layer consists of two main components: a causal self-attention mechanism and a feed-forward network, both wrapped with residual connections. The causal attention ensures that predictions for each position can only depend on previous positions, which is essential for autoregressive generation.…