diff --git a/intermediate_source/TP_tutorial.rst b/intermediate_source/TP_tutorial.rst
index 91e64a8748..4108e72b02 100644
--- a/intermediate_source/TP_tutorial.rst
+++ b/intermediate_source/TP_tutorial.rst
@@ -333,7 +333,7 @@ This 2-D parallelism pattern can be easily expressed via a 2-D DeviceMesh, and w
 
     from torch.distributed.device_mesh import init_device_mesh
     from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel, parallelize_module
-    from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+    from torch.distributed.fsdp import fully_shard
 
     # i.e. 2-D mesh is [dp, tp], training on 64 GPUs that performs 8 way DP and 8 way TP
     mesh_2d = init_device_mesh("cuda", (8, 8))
@@ -347,7 +347,7 @@ This 2-D parallelism pattern can be easily expressed via a 2-D DeviceMesh, and w
     # apply Tensor Parallel intra-host on tp_mesh
     model_tp = parallelize_module(model, tp_mesh, tp_plan)
     # apply FSDP inter-host on dp_mesh
-    model_2d = FSDP(model_tp, device_mesh=dp_mesh, use_orig_params=True, ...)
+    model_2d = fully_shard(model_tp, mesh=dp_mesh, ...)
 
 
 This would allow us to easily apply Tensor Parallel within each host (intra-host) and apply FSDP across hosts (inter-hosts), with **0-code changes** to the Llama model.