What is recorded here?
Extended the binary-weight design with absolute-mean ternary quantization into negative, zero and positive values, paired with an adapted Transformer training recipe and eight-bit activations.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The 1.58-bit description concerns ternary weight information, not the precision of the complete training system or every stored tensor. Matching full-precision quality is an empirical tested regime, not all model sizes/tasks; original BitNet and earlier ternary networks remain prior inputs.
Read the original work
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
- Ma et al.: BitNet b1.58 ternary-weight language-model recipe — official project source
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Shuming Ma; Hongyu Wang; Lingxiao Ma; Lei Wang; Wenhui Wang; Shaohan Huang; Li Dong; Ruiping Wang; Jilong Xue; Furu Wei
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Ma et al.: BitNet b1.58 ternary-weight language-model recipe
Extended the binary-weight design with absolute-mean ternary quantization into negative, zero and positive values, paired with an adapted Transformer training recipe and eight-bit activations.
Scientific scope
The 1.58-bit description concerns ternary weight information, not the precision of the complete training system or every stored tensor. Matching full-precision quality is an empirical tested regime, not all model sizes/tasks; original BitNet and earlier ternary networks remain prior inputs.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Shuming Ma; Hongyu Wang; Lingxiao Ma; Lei Wang; Wenhui Wang; Shaohan Huang; Li Dong; Ruiping Wang; Jilong Xue; Furu Wei
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.