Update README.md (#903)

fixing link that got broken when fpx -> floatx dir name change

torchao/quantization/README.md

@@ -129,7 +129,7 @@ from torchao.quantization import quantize_, fpx_weight_only
 quantize_(model, fpx_weight_only(3, 2))
 ```
 
-You can find more information [here](../dtypes/fpx/README.md). It should be noted where most other TorchAO apis and benchmarks have focused on applying techniques on top of a bf16 model, performance, fp6 works primarily with the fp16 dtype.
+You can find more information [here](../dtypes/floatx/README.md). It should be noted where most other TorchAO apis and benchmarks have focused on applying techniques on top of a bf16 model, performance, fp6 works primarily with the fp16 dtype.
 
 ## Affine Quantization Details
 Affine quantization refers to the type of quantization that maps from high precision floating point numbers to quantized numbers (low precision integer or floating point dtypes) with an affine transformation, i.e.: `quantized_val = high_preicsion_float_val / scale + zero_point` where `scale` and `zero_point` are quantization parameters for some granularity and based on some data (also some dtypes may not require a `zero_point`). Each of the techniques in the above section qualify as Affine Quantization.