BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs

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.

@article{wang2025_2504.18415,
  title={ BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs },
  author={ Hongyu Wang and Shuming Ma and Furu Wei },
  journal={arXiv preprint arXiv:2504.18415},
  year={ 2025 }
}