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Beyond Language Models: Byte Models are Digital World Simulators

Shangda Wu, Xu Tan, Zili Wang, Rui Wang, Xiaobing Li, Maosong Sun

Abstract

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.

Beyond Language Models: Byte Models are Digital World Simulators

Abstract

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.
Paper Structure (24 sections, 7 equations, 5 figures, 7 tables)

This paper contains 24 sections, 7 equations, 5 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Language Models
  4. Byte Models
  5. Methodology
  6. Model Architecture
  7. Training Objectives
  8. Applications
  9. Digital Media Processing
  10. Algorithm and Hardware Simulation
  11. Experiments
  12. Settings
  13. Digital Media Processing
  14. Baselines
  15. Results
  16. ...and 9 more sections

Figures (5)

  • Figure 1: The bGPT framework simulates digital systems through native binary data, and integrates diverse data types into a single model, treating everything as a byte sequence. This approach simplifies integration and expands application possibilities in the digital world.
  • Figure 2: bGPT segments byte sequences into patches, predicts next patch features with a patch-level decoder, and reconstructs bytes within patches using these features with a byte-level decoder.
  • Figure 3: Training and evaluation loss curves for different data scales in bGPT performance across epochs.
  • Figure 4: Sheet music example corresponding to the ABC notation of a folk tune in D major.
  • Figure 5: Sheet music example corresponding to the MIDI of the same folk tune presented in Fig. 4.