Llama3.2 quantization
Quantization is a technique used in machine learning to reduce the size of a model by representing its weights and activations with lower-precision data types, typically reducing the precision from 32-bit floating-point (FP32) to 16-bit (FP16) or even 8-bit (INT8) formats. This process makes the model smaller and more efficient regarding memory and computational requirements while aiming to maintain acceptable performance.