BitNet b1.58 2B4T Technical Report
Research paper by Shuming Ma and collaborators. Published 2025-04-16. Topics: BitNet, Large Language Model, 1-bit, 2-billion parameters, corpus.
Resource information
- Authors
- Shuming Ma, Hongyu Wang, Shaohan Huang, Xingxing Zhang, Ying Hu, Ting Song, Yan Xia, Furu Wei
- Research area
- Language technology
- Application domain
- General AI research
- AI methods
- Large language models
- Research topics
- cs.CL, cs.LG
- arXiv record date
- 2025-04-25
- Publication date
- 2025-04-16
- Publication
- arXiv
- Upstream last modified
- Apr 28, 2025
- Last record update
- Nov 3, 2026
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.