Quantization is a technique used in machine learning model compression to reduce the number of bits used to represent the weights and activations of a model, without significant loss in accuracy. The INT4 quantization scheme is a particular type of quantization that is used in transformer model compression.

In INT4 quantization, the values are represented using only 4 bits. Specifically, the range of the original values is divided into 16 equally spaced bins, and each bin is assigned a unique 4-bit code. The values are then rounded to the center of their corresponding bin, and the 4-bit code is used to represent the rounded value.

This can be an effective compression technique because the weights and activations in a transformer model tend to have a relatively small range of values, and INT4 quantization is often sufficient to represent these values with high accuracy. Additionally, using 4-bit values instead of the usual 32-bit floating-point values can significantly reduce the memory required to store the model, which can be important in applications where memory is limited, such as mobile devices or embedded systems.

However, it's worth noting that using INT4 quantization can sometimes result in a loss of accuracy compared to using full-precision values, especially for models with a large number of weights and complex architectures. Therefore, it's important to carefully evaluate the trade-off between accuracy and memory usage when using INT4 quantization, and to experiment with different quantization schemes to find the best approach for a particular model and application.
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