update the tested qnn version (#5903)

Summary:
Pull Request resolved: #5903

As title, they were tested in more recent version. 2.13 likely breaks

Reviewed By: mergennachin

Differential Revision: D63922556

fbshipit-source-id: 926fe6fd172d65f02d470f689016c687f1504647

Author: cccclai

examples/qualcomm/README.md

@@ -4,7 +4,7 @@ This directory contains examples for some AI models.
 
 We have seperated the example scripts into the following subfolders, please refer to [README.md](../../backends/qualcomm/README.md) for the example scripts' directory structure:
 
-1. executor_runner: This folder contains a general executor runner capable of running most of the models. As a rule of thumb, if a model does not have its own customized runner, execute the model using [executor_runner](./executor_runner/qnn_executor_runner.cpp). On the other hand, if a model has its own runner, such as [llama2](./oss_scripts/llama2/qnn_llama_runner.cpp), use the customized runner to execute the model. Customized runner should be located under the same folder as the model's python script. 
+1. executor_runner: This folder contains a general executor runner capable of running most of the models. As a rule of thumb, if a model does not have its own customized runner, execute the model using [executor_runner](./executor_runner/qnn_executor_runner.cpp). On the other hand, if a model has its own runner, such as [llama2](./oss_scripts/llama2/qnn_llama_runner.cpp), use the customized runner to execute the model. Customized runner should be located under the same folder as the model's python script.
 
 2. oss_scripts: OSS stands for Open Source Software. This folder contains python scripts for open source models. Some models under this folder might also have their own customized runner.
    For example, [llama2](./oss_scripts/llama2/qnn_llama_runner.cpp) contains not only the python scripts to prepare the model but also a customized runner for executing the model.
@@ -83,7 +83,7 @@ pip install scikit-learn pandas
 
 ## Limitation
 
-1. QNN 2.13 is used for all examples. Newer or older QNN might work,
+1. QNN 2.24 is used for all examples. Newer or older QNN might work,
 but the performance and accuracy number can differ.
 
 2. The mobilebert example is on QNN HTP fp16, which is only supported by a limited