diff --git a/README.md b/README.md
index 0043df22d..612fe1f31 100644
--- a/README.md
+++ b/README.md
@@ -49,14 +49,14 @@ And a quick crash course on inference quantization to help parse the above table
 
 Sparsifying your model is also a 1 liner that should work on any model with an `nn.Linear`. We find that sparsity works best on compute bound models like SAM, specifically the MLP layers.
 ```python
-from torchao.sparsity import sparsify, semi_sparse_weight()
+from torchao.sparsity import sparsify_, semi_sparse_weight
 
 m = sparsify_(m, semi_sparse_weight())
 ```
 Sparsity can also be composed with int8 dynamic quantization for further speedups:
 
 ```python
-from torchao.sparsity import sparsify, int8_dynamic_activation_int8_semi_sparse_weight
+from torchao.sparsity import sparsify_, int8_dynamic_activation_int8_semi_sparse_weight
 
 m = sparsify_(m, int8_dynamic_activation_int8_semi_sparse_weight())
 ```