diff --git a/docs/pytorch_converter/README.md b/docs/pytorch_converter/README.md
index 513e4e81..8111ad3b 100644
--- a/docs/pytorch_converter/README.md
+++ b/docs/pytorch_converter/README.md
@@ -120,13 +120,11 @@ edge_model(*inputs_2, signature_name="input2")
 ## Quantization
 
 Following is the code snippet to quantize a model with [PT2E
-quantization](https://pytorch.org/tutorials/prototype/quantization_in_pytorch_2_0_export_tutorial.html)
+quantization](https://docs.pytorch.org/ao/stable/tutorials_source/pt2e_quant_ptq.html)
 using the `ai_edge_torch` backend.
 
 ```python
 from torch.ao.quantization.quantize_pt2e import prepare_pt2e, convert_pt2e
-from torch._export import capture_pre_autograd_graph
-
 from ai_edge_torch.quantize.pt2e_quantizer import get_symmetric_quantization_config
 from ai_edge_torch.quantize.pt2e_quantizer import PT2EQuantizer
 from ai_edge_torch.quantize.quant_config import QuantConfig
@@ -135,7 +133,12 @@ pt2e_quantizer = PT2EQuantizer().set_global(
     get_symmetric_quantization_config(is_per_channel=True, is_dynamic=True)
 )
 
-pt2e_torch_model = capture_pre_autograd_graph(torch_model, sample_args)
+# > For pytorch 2.6+
+pt2e_torch_model = torch.export.export(torch_model, sample_args).module()
+# > For pytorch 2.5 and before
+# from torch._export import capture_pre_autograd_graph
+# pt2e_torch_model = capture_pre_autograd_graph(torch_model, sample_args)
+
 pt2e_torch_model = prepare_pt2e(pt2e_torch_model, pt2e_quantizer)
 
 # Run the prepared model with sample input data to ensure that internal observers are populated with correct values