Update 2023-10-04-pytorch-2-1.md (#1474)

Co-authored-by: Alena Davis <[email protected]>

Author: jerryzh168

_posts/2023-10-04-pytorch-2-1.md

@@ -90,7 +90,7 @@ For more information, please see the tutorial [here](https://pytorch.org/tutoria
 
 **\[Prototype] _torch.export_-based Quantization**
 
-_torch.ao.quantization_ now supports post-training static quantization on PyTorch2-based _torch.export_ flows.  This includes support for built-in _XNNPACK_ and _X64Inductor_ _Quantizer_, as well as the ability to specify one’s own _Quantizer_.
+_torch.ao.quantization_ now supports quantization on PyTorch2-based _torch.export_ flows.  This includes support for built-in _XNNPACK_ and _X64Inductor_ _Quantizer_, as well as the ability to specify one’s own _Quantizer_.
 
 For an explanation on post-training static quantization with torch.export, see [this tutorial](https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html), for quantization-aware training for static quantization with torch.export, see [this tutorial](https://pytorch.org/tutorials/prototype/pt2e_quant_qat.html).