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BitNet b1.58 matrix multiplication shows ternary weights enabling faster neural network computation.

Machine Learning Research

Low Precision, High Performance: Researchers at Microsoft and Tsinghua researchers propose 1.58-bit AI model that rivals full-precision competitors

Reducing the number of bits used to represent each parameter in a neural network from, say, 16 bits to 8 bits shrinks the network’s size and boosts its speed. Researchers took this approach to an extreme: They built a competitive large language model whose weights are limited to three values.

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