Quantization-Aware Training (QAT) is a powerful technique that minimizes the trade-off between computational efficiency and model accuracy, enabling the deployment of deep learning models on resource-constrained devices with near-full precision performance. In the realm of advanced AI workflows, PyTorch, combined with JIT (Just-In-Time) compilation and TorchScript, provides a cutting-edge framework for implementing QAT. This technical … Continue reading Quantization-Aware Training (QAT): Advanced Techniques with JIT, TorchScript, and PyTorch