- Quantization is a technique used in machine learning to reduce the precision of the numbers used in a model. - In simpler terms, it means that instead of using numbers with a high degree of accuracy, we use numbers with lower accuracy. - This technique is used to reduce the size of the model, which in turn speeds up computation. - The reason for this is that using numbers with lower accuracy requires less memory and computational power. - For example, instead of using a floating-point number with 32 bits of precision, we can use an 8-bit integer to represent the same value. - This reduces the memory required to store the number by a factor of 4 and reduces the computational power required to perform operations on the number. - Quantization is particularly useful in the context of large language models, where the size of the model can be a limiting factor in terms of computational resources. - By reducing the precision of the numbers used in the model, we can reduce the size of the model and make it more efficient to train and use. - However, it is important to note that quantization can also have a negative impact on the accuracy of the model, as using numbers with lower precision can lead to rounding errors and other inaccuracies. - Therefore, it is important to carefully balance the trade-off between model size and accuracy when using quantization. https://github.com/mit-han-lab/smoothquant