From 7f1c14becfa7bf552ad6871dc8c39dc97c5d155b Mon Sep 17 00:00:00 2001
From: sekyondaMeta <127536312+sekyondaMeta@users.noreply.github.com>
Date: Thu, 23 Jan 2025 11:21:38 -0500
Subject: [PATCH] Update FSDP_tutorial.rst

Link no longer exists so giving credit to creator instead
---
 intermediate_source/FSDP_tutorial.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/intermediate_source/FSDP_tutorial.rst b/intermediate_source/FSDP_tutorial.rst
index ce104889860..8e5217c64a8 100644
--- a/intermediate_source/FSDP_tutorial.rst
+++ b/intermediate_source/FSDP_tutorial.rst
@@ -11,7 +11,7 @@ It also comes with considerable engineering complexity to handle the training of
 `PyTorch FSDP <https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/>`__, released in PyTorch 1.11 makes this easier.
 
 In this tutorial, we show how to use `FSDP APIs <https://pytorch.org/docs/stable/fsdp.html>`__, for simple MNIST models that can be extended to other larger models such as `HuggingFace BERT models <https://huggingface.co/blog/zero-deepspeed-fairscale>`__, 
-`GPT 3 models up to 1T parameters <https://pytorch.medium.com/training-a-1-trillion-parameter-model-with-pytorch-fully-sharded-data-parallel-on-aws-3ac13aa96cff>`__ . The sample DDP MNIST code has been borrowed from `here <https://github.com/yqhu/mnist_examples>`__. 
+`GPT 3 models up to 1T parameters <https://pytorch.medium.com/training-a-1-trillion-parameter-model-with-pytorch-fully-sharded-data-parallel-on-aws-3ac13aa96cff>`__ . The sample DDP MNIST code courtesy of `Patrick Hu <https://github.com/yqhu/>`_. 
 
 
 How FSDP works