From 052100a722d2dc548d4add641397eb926c802e46 Mon Sep 17 00:00:00 2001
From: wangxd <58022314+wangxd-xlnx@users.noreply.github.com>
Date: Tue, 12 Sep 2023 10:38:28 +0800
Subject: [PATCH] Update gpu_model_example.md

---
 docs/2_model_setup/gpu_model_example.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/2_model_setup/gpu_model_example.md b/docs/2_model_setup/gpu_model_example.md
index 4b85cca..95f8591 100644
--- a/docs/2_model_setup/gpu_model_example.md
+++ b/docs/2_model_setup/gpu_model_example.md
@@ -21,7 +21,7 @@
 
 UIF accelerates deep learning inference applications on all AMD compute platforms for popular machine learning frameworks, including TensorFlow, PyTorch, and ONNXRT. UIF 1.2 extends the support to AMD Radeon™ GPUs in addition to AMD Instinct™ GPUs. Currently, [MIGraphX](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX) is the acceleration library for Deep Learning Inference running on AMD Instinct GPUs. 
 
-The following example takes a PyTorch ResNet-50-v1.5 model selected from UIF Model Zoo as an example to show how it works on different GPU platforms.
+The following example takes a [PyTorch ResNet-50-v1.5 model](https://github.com/amd/UIF/blob/main/docs/2_model_setup/model-list/pt_resnet50v1.5_1.1_M2.6/model.yaml) selected from UIF Model Zoo as an example to show how it works on different GPU platforms.
 
 **Note:**  The model tuning time on a MI210 device is long (around three hours). With MI210, it is recommended to skip this step and use the YModel provided in the model packages.