low-precision models
Low-precision models refer to machine learning models that use reduced precision, such as 16-bit or lower, to represent numerical values during computation. This approach is used to optimize the performance and memory usage of the model. While it may sacrifice some accuracy compared to higher precision models, it can still yield satisfactory results with faster training and inference times.
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