1 Bit Quantization with BitLinear from Zeta! 

DEMO LINK: github.com/kye...
DOCS: zeta.apac.ai/e...
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$pip install zetascale
1 Bit Quantization is a process in which continuous data is converted into a discrete representation with only two possible output values: 0 and 1. This technique is often used in various fields, including signal processing and machine learning, as it simplifies data representation and reduces computational complexity.
BitLinear from Zeta is a framework that enables efficient 1 Bit Quantization for neural networks. It incorporates a combination of advanced algorithms and hardware optimization techniques to achieve accurate binary representation while minimizing the loss of information. By utilizing BitLinear, neural networks can be compressed, resulting in reduced storage requirements and improved computational efficiency.
Overall, 1 Bit Quantization with BitLinear from Zeta offers a promising solution for optimizing neural networks, providing more efficient processing and enabling the deployment of deep learning models in resource-constrained environments.

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