BitNet v2: Native 4-bit Activations with Hadamard Transformation for
  1-bit LLMs
Authors: name: Hongyu Wang, name: Shuming Ma, name: Furu Wei

Efficient deployment of 1-bit Large Language Models (LLMs) is hindered by
activation outliers, which complicate quantization to low bit-widths. We
introduce BitNet v2, a novel framework enabling native 4-bit activation
quantization for 1-bit LLMs. To tackle outliers in attention and feed-forward
network activations, we propose H-BitLinear, a module applying an online
Hadamard transformation prior to activation quantization. This transformation
smooths sharp activation distributions into more Gaussian-like forms, suitable
for low-bit representation. Experiments show BitNet v2 trained from scratch
with 8-bit activations matches BitNet b1.58 performance. Crucially, BitNet v2
achieves minimal performance degradation when trained with native 4-bit
activations, significantly reducing memory footprint and computational cost for
batched inference.