Remove LLaMA demo app.

.github/workflows/apple.yml

@@ -17,7 +17,6 @@ on:
       - scripts/build_apple_llm_demo.sh
       - scripts/create_frameworks.sh
       - .ci/scripts/test_ios_ci.sh
-      - examples/demo-apps/apple_ios/**
       - extension/apple/**
       - extension/benchmark/apple/**
       - extension/module/**

.lintrunner.toml

@@ -73,7 +73,6 @@ exclude_patterns = [
     '**/third-party/**',
     # NB: Objective-C is not supported
     'examples/apple/**',
-    'examples/demo-apps/apple_ios/**',
     'examples/demo-apps/react-native/rnllama/ios/**',
     'extension/apple/**',
     'extension/llm/apple/**',

README-wheel.md

@@ -25,6 +25,6 @@ tutorials and documentation. Here are some starting points:
 * [Exporting to ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial)
   * Learn the fundamentals of exporting a PyTorch `nn.Module` to ExecuTorch, and
     optimizing its performance using quantization and hardware delegation.
-* Running LLaMA on [iOS](docs/source/llm/llama-demo-ios.md) and [Android](docs/source/llm/llama-demo-android.md) devices.
+* Running etLLM on [iOS](https://github.com/meta-pytorch/executorch-examples/tree/main/llm/apple) and [Android](docs/source/llm/llama-demo-android.md) devices.
   * Build and run LLaMA in a demo mobile app, and learn how to integrate models
     with your own apps.

backends/apple/mps/setup.md

@@ -16,7 +16,7 @@ The MPS backend device maps machine learning computational graphs and primitives
 * [Setting up ExecuTorch](../../../docs/source/getting-started-setup.rst)
 * [Building ExecuTorch with CMake](../../../docs/source/using-executorch-cpp.md#building-with-cmake)
 * [ExecuTorch iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
-* [ExecuTorch iOS LLaMA Demo App](../../../docs/source/llm/llama-demo-ios.md)
+* [ExecuTorch LLM iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/llm/apple)
 :::
 ::::
 

docs/source/backends-mps.md

@@ -16,7 +16,7 @@ The MPS backend device maps machine learning computational graphs and primitives
 * [Getting Started](getting-started.md)
 * [Building ExecuTorch with CMake](using-executorch-building-from-source.md)
 * [ExecuTorch iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
-* [ExecuTorch iOS LLaMA Demo App](llm/llama-demo-ios.md)
+* [ExecuTorch LLM iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/llm/apple)
 :::
 ::::
 

docs/source/llm/getting-started.md

@@ -23,4 +23,4 @@ Deploying LLMs to ExecuTorch can be boiled down to a two-step process: (1) expor
 - [Running with C++](run-with-c-plus-plus.md)
 - [Running on Android (XNNPack)](llama-demo-android.md)
 - [Running on Android (Qualcomm)](build-run-llama3-qualcomm-ai-engine-direct-backend.md)
-- [Running on iOS](llama-demo-ios.md)
+- [Running on iOS](https://github.com/meta-pytorch/executorch-examples/tree/main/llm/apple)

docs/source/llm/run-on-ios.md

@@ -123,4 +123,4 @@ runner.stop()
 
 ## Demo
 
-Get hands-on with our [LLaMA iOS Demo App](llama-demo-ios.md) to see the LLM runtime APIs in action.
+Get hands-on with our [etLLM iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/llm/apple) to see the LLM runtime APIs in action.

examples/README.md

@@ -21,7 +21,7 @@ examples
 |   └── mps                           # Contains end-to-end demos of MPS backend
 ├── arm                               # Contains demos of the Arm TOSA and Ethos-U NPU flows
 ├── qualcomm                          # Contains demos of Qualcomm QNN backend
-â├── samsung                          # Contains demos of Samsung Exynos backend
+�├── samsung                          # Contains demos of Samsung Exynos backend
 ├── cadence                           # Contains demos of exporting and running a simple model on Xtensa DSPs
 ├── third-party                       # Third-party libraries required for working on the demos
 └── README.md                         # This file
@@ -34,7 +34,7 @@ A user's journey may commence by exploring the demos located in the [`portable/`
 
 ## Demos Apps
 
-Explore mobile apps with ExecuTorch models integrated and deployable on [Android](demo-apps/android) and [iOS](demo-apps/apple_ios). This provides end-to-end instructions on how to export Llama models, load on device, build the app, and run it on device.
+Explore mobile apps with ExecuTorch models integrated and deployable on [Android](demo-apps/android) and [iOS](https://github.com/meta-pytorch/executorch-examples/tree/main/llm/apple). This provides end-to-end instructions on how to export Llama models, load on device, build the app, and run it on device.
 
 For specific details related to models and backend, you can explore the various subsections.