diff --git a/intermediate_source/FSDP_tutorial.rst b/intermediate_source/FSDP_tutorial.rst
index ca54000218b..b0cdc19be23 100644
--- a/intermediate_source/FSDP_tutorial.rst
+++ b/intermediate_source/FSDP_tutorial.rst
@@ -240,7 +240,7 @@ We showcase how to convert a full state dict into a DTensor state dict for loadi
 * For the 1st time, it creates checkpoints for the model and optimizer
 * For the 2nd time, it loads from the previous checkpoint to resume training
 
-**Loading state dicts**: We initialize the model under meta device and call ``fully_shard`` to convert ``model.parameters()`` from plain ``torch.Tensor`` to DTensor. After reading the full state dict from torch.load, we can call `distributed_tensor <https://docs.pytorch.org/docs/stable/distributed.tensor.html#torch.distributed.tensor.distribute_tensor>`_ to convert plain ``torch.Tensor`` into DTensor, using the same placements and device mesh from ``model.state_dict()``. Finally we can call `model.load_state_dict <https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.load_state_dict>`_ to load DTensor state dicts into the model.
+**Loading state dicts**: We initialize the model under meta device and call ``fully_shard`` to convert ``model.parameters()`` from plain ``torch.Tensor`` to DTensor. After reading the full state dict from torch.load, we can call `distribute_tensor <https://docs.pytorch.org/docs/stable/distributed.tensor.html#torch.distributed.tensor.distribute_tensor>`_ to convert plain ``torch.Tensor`` into DTensor, using the same placements and device mesh from ``model.state_dict()``. Finally we can call `model.load_state_dict <https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.load_state_dict>`_ to load DTensor state dicts into the model.
 
 .. code-block:: python