📄 中文摘要
在前一篇关于量化感知训练（QAT）的博客中，介绍了TorchAO中针对边缘设备的大型语言模型的初始QAT流程，特别是与ExecuTorch的结合。此后，流程得到了扩展，增加了更多的功能和优化，以提高模型在资源受限环境下的性能。新版本的QAT流程不仅支持更广泛的模型架构，还引入了新的量化策略，旨在减少模型的计算和存储开销，同时保持其推理精度。通过这些改进，开发者能够更高效地在边缘设备上部署深度学习模型，满足实时应用的需求。
TorchAO中的量化感知训练（II）
出处: Quantization-Aware Training in TorchAO (II)
发布: 2026年3月4日
📄 English Summary
Quantization-Aware Training in TorchAO (II)
The previous blog on Quantization-Aware Training (QAT) introduced the initial QAT flow in TorchAO for large language models targeting edge devices, particularly in conjunction with ExecuTorch. Since then, the flow has been extended to include additional features and optimizations aimed at enhancing model performance in resource-constrained environments. The new version of the QAT flow supports a wider range of model architectures and introduces novel quantization strategies designed to reduce computational and storage overhead while maintaining inference accuracy. These improvements enable developers to deploy deep learning models more efficiently on edge devices, meeting the demands of real-time applications.