Beyond Language Models: Byte Models are Digital World Simulators
Shangda Wu Xu Tan Zili Wang Rui Wang Xiaobing Li Maosong Sun
Traditional deep learning often overlooks bytes, the basic units of the
digital world, where all forms of information and operations are encoded and
manipulated in binary format. Inspired by the success of next token prediction
in natural language processing, we introduce bGPT, a model with next byte
prediction to simulate the digital world. bGPT matches specialized models in
performance across various modalities, including text, audio, and images, and
offers new possibilities for predicting, simulating, and diagnosing algorithm
or hardware behaviour. It has almost flawlessly replicated the process of
converting symbolic music data, achieving a low error rate of 0.0011 bits per
byte in converting ABC notation to MIDI format. In addition, bGPT demonstrates
exceptional capabilities in simulating CPU behaviour, with an accuracy
exceeding 99.99% in executing various operations. Leveraging next byte
prediction, models like bGPT can directly learn from vast binary data,
effectively simulating the intricate patterns of the digital world.