1-bit LLMs by Microsoft | Implementing GPT in 60 Lines

Microsoft released BitNet b1.58, a new Large Language Model (LLM) using 1.58 bits per parameter, reducing computational demands significantly while maintaining performance. 

Unlike traditional 16-bit models, BitNet employs ternary values (-1, 0, 1), slashing GPU memory use and energy consumption by up to 3.5 times and 71 times respectively, without sacrificing model accuracy.

BitNet b1.58 achieves comparable or superior results to FP16 models like LLaMA 3B in perplexity and various language tasks starting from a 3 billion parameter size. 

The reduction in precision to 1-bit minimizes the need for energy-heavy floating-point operations, particularly in matrix multiplication, speeding up computations and lowering energy costs.

This model’s efficiency improves as it scales, offering significant performance enhancements, especially in larger models. For instance, at 70 billion parameters, BitNet is over four times faster than traditional models, increasing throughput and reducing latency.

This tutorial provides a step-by-step guide to creating a simplified GPT model using only 60 lines of NumPy. It assumes familiarity with Python, NumPy, and basic neural network training concepts. 

The focus is on constructing a basic yet complete version of the GPT architecture for educational purposes, highlighting key components such as input/output functions, text generation techniques, and the training process. 

It covers the GPT’s fundamental elements, including embeddings, decoder stacks, and attention mechanisms.