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Computer Science > Computation and Language

arXiv:2504.12285 (cs)
[Submitted on 16 Apr 2025]

Title:BitNet b1.58 2B4T Technical Report

Authors:Shuming Ma, Hongyu Wang, Shaohan Huang, Xingxing Zhang, Ying
Hu, Ting Song, Yan Xia, Furu Wei
View a PDF of the paper titled BitNet b1.58 2B4T Technical Report, by
Shuming Ma and 7 other authors
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    Abstract:We introduce BitNet b1.58 2B4T, the first open-source,
    native 1-bit Large Language Model (LLM) at the 2-billion
    parameter scale. Trained on a corpus of 4 trillion tokens, the
    model has been rigorously evaluated across benchmarks covering
    language understanding, mathematical reasoning, coding
    proficiency, and conversational ability. Our results demonstrate
    that BitNet b1.58 2B4T achieves performance on par with leading
    open-weight, full-precision LLMs of similar size, while offering
    significant advantages in computational efficiency, including
    substantially reduced memory footprint, energy consumption, and
    decoding latency. To facilitate further research and adoption,
    the model weights are released via Hugging Face along with
    open-source inference implementations for both GPU and CPU
    architectures.

Comments: Work in progress
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2504.12285 [cs.CL]
          (or arXiv:2504.12285v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2504.12285
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shuming Ma [view email]
[v1] Wed, 16 Apr 2025 17:51:43 UTC (67 KB)
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