diff --git a/examples/README.md b/examples/README.md
index 3bebd3b860f..1ad0018c981 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -1,2 +1,17 @@
 ## Overview
-This repo aims to provide some basic examples of how to run an existing pytorch model with PyTorch/XLA. train_resnet_base.py is a minimal trainer to run ResNet50 with fake data on a single device. Other examples will import the train_resnet_base and demonstrate how to enable different features(distributed training, profiling, dynamo etc) on PyTorch/XLA.The objective of this repository is to offer fundamental examples of executing an existing PyTorch model utilizing PyTorch/XLA. train_resnet_base.py acts as a bare-bones trainer for running ResNet50 with simulated data on an individual device. Additional examples will import train_resnet_base and illustrate how to activate various features (e.g., distributed training, profiling, dynamo) on PyTorch/XLA.
+This repo aims to provide some basic examples of how to run an existing pytorch model with PyTorch/XLA. `train_resnet_base.py` is a minimal trainer to run ResNet50 with fake data on a single device. `train_decoder_only_base.py` is similar to `train_resnet_base.py` but with a decoder only model.
+
+Other examples will import the `train_resnet_base` or `train_decoder_only_base` and demonstrate how to enable different features(distributed training, profiling, dynamo etc) on PyTorch/XLA.The objective of this repository is to offer fundamental examples of executing an existing PyTorch model utilizing PyTorch/XLA.
+
+## Setup
+Follow our [README](https://github.com/pytorch/xla#getting-started) to install latest release of torch_xla. Check out this [link](https://github.com/pytorch/xla#python-packages) for torch_xla at other versions. To install the nightly torchvision(required for the resnet) you can do
+
+```shell
+pip install --no-deps --pre torchvision -i https://download.pytorch.org/whl/nightly/cu118
+```
+
+## Run the example
+You can run all models directly. Only environment you want to set is `PJRT_DEVICE`.
+```
+PJRT_DEVICE=TPU python fsdp/train_decoder_only_fsdp_v2.py
+```
diff --git a/examples/debug/train_resnet_profile.py b/examples/debug/train_resnet_profile.py
index 886c25f65b7..158c138dba7 100644
--- a/examples/debug/train_resnet_profile.py
+++ b/examples/debug/train_resnet_profile.py
@@ -1,5 +1,5 @@
 import os
-import os
+import sys
 example_folder = os.path.dirname(os.path.dirname(os.path.abspath(sys.argv[0])))
 sys.path.append(example_folder)
 from train_resnet_base import TrainResNetBase
@@ -13,9 +13,11 @@
 if __name__ == '__main__':
   base = TrainResNetBase()
   profile_port = 9012
+  # you can also set profile_logdir to a gs bucket, for example
+  # profile_logdir = "gs://your_gs_bucket/profile"
   profile_logdir = "/tmp/profile/"
   duration_ms = 30000
-  assert os.path.exists(profile_logdir)
+  assert profile_logdir.startswith('gs://') or os.path.exists(profile_logdir)
   server = xp.start_server(profile_port)
   # Ideally you want to start the profile tracing after the initial compilation, for example
   # at step 5.