Update README.md

apple/ExecuTorchDemo/README.md

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 This app uses the [MobileNet v3](https://pytorch.org/vision/main/models/mobilenetv3.html) model to process live camera images leveraging three different backends: [XNNPACK](https://github.com/google/XNNPACK), [Core ML](https://developer.apple.com/documentation/coreml) and [Metal Performance Shaders (MPS)](https://developer.apple.com/documentation/metalperformanceshaders) (Xcode 15+ and iOS 17+ only).
 
+Please refer to the [documentation](https://pytorch.org/executorch/main/demo-apps-ios.html) on how to build and run this app.
+
 <p align="center">
   <img src="https://github.com/user-attachments/assets/69f4cc2c-a95e-4e01-ba4d-7e40791716c8" width="50%">
 </p>
-
-## Prerequisites
-
-Before we start, make sure you have the following tools installed:
-
-### 1. Xcode 15+ and Command Line Tools
-
-Install Xcode 15+ from the
-[Mac App Store](https://apps.apple.com/app/xcode/id497799835) and then install
-the Command Line Tools using the terminal:
-
-```bash
-xcode-select --install
-```
-
-### 2. Python 3.10+
-
-Python 3.10 or above, along with `pip3`, should be pre-installed on MacOS 13.5+.
-If needed, [download Python](https://www.python.org/downloads/macos/) and
-install it. Verify the Python and pip versions using these commands:
-
-```bash
-which python3 pip3
-python3 --version
-pip3 --version
-```
-
-## Models and Labels
-
-Now, let's move on to exporting and bundling the MobileNet v3 model.
-
-### 1. Set Up ExecuTorch
-
-Clone ExecuTorch and configure the basic environment:
-
-```bash
-git clone https://github.com/pytorch/executorch.git --depth 1 && cd executorch && \
-python3 -m venv .venv && source .venv/bin/activate && pip3 install --upgrade pip && cd - && \
-./executorch/install_executorch.sh
-```
-
-### 2. Install Backend Dependencies
-
-Install additional dependencies for Core ML and MPS backends:
-
-```bash
-./executorch/backends/apple/coreml/scripts/install_requirements.sh && \
-./executorch/backends/apple/mps/install_requirements.sh
-```
-
-### 3. Export Model
-
-Export the MobileNet v3 model with Core ML, MPS and XNNPACK backends:
-
-```bash
-MODEL_NAME="mv3"
-cd executorch && \
-python3 -m examples.portable.scripts.export --model_name="$MODEL_NAME" && \
-python3 -m examples.apple.coreml.scripts.export --model_name="$MODEL_NAME" && \
-python3 -m examples.apple.mps.scripts.mps_example --model_name="$MODEL_NAME" && \
-python3 -m examples.xnnpack.aot_compiler --model_name="$MODEL_NAME" --delegate && \
-cd -
-```
-
-Move the exported model files (those with `.pte` extension) to a specific location where the Demo App will pick them up:
-
-```bash
-mkdir -p apple/ExecuTorchDemo/ExecuTorchDemo/Resources/Models/MobileNet/ && \
-mv executorch/"$MODEL_NAME"*.pte apple/ExecuTorchDemo/ExecuTorchDemo/Resources/Models/MobileNet/
-```
-
-### 4. Download Labels
-
-Download the MobileNet model labels required for image classification:
-
-```bash
-curl https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt \
-  -o apple/ExecuTorchDemo/ExecuTorchDemo/Resources/Models/MobileNet/imagenet_classes.txt
-```
-
-## Final Steps
-
-Now, we just need to open the project in Xcode, run the tests, and finally run the app.
-Double-click on the project file under `apple/ExecuTorchDemo` to open it with Xcode, or run the command:
-
-```bash
-open apple/ExecuTorchDemo/ExecuTorchDemo.xcodeproj
-```
-
-### 1. Run Tests
-
-You can run tests on Simulaltor directly in Xcode with `Cmd + U` or use the command line:
-
-```bash
-xcrun simctl create executorch "iPhone 15" && \
-xcodebuild clean test \
-     -project apple/ExecuTorchDemo/ExecuTorchDemo.xcodeproj \
-     -scheme App \
-     -destination name=executorch && \
-xcrun simctl delete executorch
-```
-
-### 2. Run App
-
-Finally, connect the device, set up Code Signing in Xcode, and then run the app using `Cmd + R`. Try installing a Release build for better performance.
-
-Learn more about integrating and running [ExecuTorch on Apple](https://pytorch.org/executorch/main/using-executorch-ios.html) platforms.