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

Hongyu Wang, Shuming Ma, Furu Wei

BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs: 51 upvotes on Hugging Face Daily Papers, #3 of 12 papers on 2025-04-28. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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