Transformer Quantization
This article explores transformer quantization techniques, a critical method for reducing the memory footprint and computational cost of transformer models. We delve into the concepts of quantization, different quantization methods (e.g., post-training quantization, quantization-aware training), and their impact on transformer performance.
Quantization aims to represent model parameters (weights and biases) with lower precision, typically integers instead of floating-point numbers. This reduces storage requirements and speeds up computations, especially on hardware optimized for integer arithmetic.
Learn how to effectively apply quantization to your transformer models and accelerate inference.
Original Article