addressing lint

Author: cmodi-meta

examples/demo-apps/android/LlamaDemo/README.md

@@ -11,7 +11,7 @@ Please dive in and start exploring our demo app today! We look forward to any fe
 From this demo app, you will learn many key concepts such as:
 * How to prepare Llama models, build the ExecuTorch library, and model inferencing across delegates
 * Expose the ExecuTorch library via JNI layer
-* Familiarity with current Executorch app-facing capabilities
+* Familiarity with current ExecuTorch app-facing capabilities
 
 The goal is for you to see the type of support ExecuTorch provides and feel comfortable with leveraging it for your use cases.
 
@@ -55,7 +55,7 @@ Select the settings widget to get started with picking a model, its parameters a
 
 Once you've selected the model, tokenizer, and model type you are ready to click on "Load Model" to have the app load the model and go back to the main Chat activity.
 <p align="center">
-      <img src="docs/screenshots/settings_menu.png" width=300>      
+      <img src="docs/screenshots/settings_menu.png" width=300>
 </p>
 
 

examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md

@@ -154,4 +154,3 @@ If the app successfully run on your device, you should see something like below:
 
 ## Reporting Issues
 If you encountered any bugs or issues following this tutorial please file a bug/issue here on [Github](https://github.com/pytorch/executorch/issues/new).
-