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Typo updates for README and Contributions (#10025)
### Summary Pulling in the non 0.6 changes from: #10006 #10016 ### Test plan md
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CONTRIBUTING.md

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Thank you for your interest in contributing to ExecuTorch! We want to make
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it easy to contribute to this project.
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## Dev Install
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│ └── <a href="runtime/platform">platform</a> - Layer between architecture specific code and portable C++.
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├── <a href="schema">schema</a> - ExecuTorch PTE file format flatbuffer schemas.
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├── <a href="scripts">scripts</a> - Utility scripts for building libs, size management, dependency management, etc.
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├── <a href="shim">shim</a> - Compatibility layer between OSS and Internal builds.
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├── <a href="shim_et">shim_et</a> - Compatibility layer between OSS and Internal builds.
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├── <a href="test">test</a> - Broad scoped end-to-end tests.
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├── <a href="third-party">third-party</a> - Third-party dependencies.
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├── <a href="tools">tools</a> - Tools for building ExecuTorch from source, for different built tools (CMake, Buck).
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## Coding Style
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Goal: Encourage standards that make it easier to read, edit, maintain, and debug
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the ExecuTorch code.
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### lintrunner
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We use [`lintrunner`](https://pypi.org/project/lintrunner/) to help make sure the
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#### C/C++ standard library usage
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**Restricted usage of the C++ standard library.**
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**Restricted usage of the C++ standard library**
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Rationale: ExecuTorch is intended to be portable to bare-metal systems that lack
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certain features, like dynamic memory, threading, and locking, required by parts
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#### C++ language features
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**Exceptions: Do not use.**
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**Exceptions: Do not use**
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- Rationale: Exceptions are not widely supported on some classes of
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microcontrollers and DSPs, and they can significantly increase binary size.
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- Rationale: The core runtime must work on systems that do not have threading
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support.
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**RTTI, dynamic_cast, and `<typeid>`: Do not use.**
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**RTTI, dynamic_cast, and `<typeid>`: Do not use**
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- Rationale: RTTI adds extra data to every virtual class. ExecuTorch doesn't
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have a strong need for `dynamic_cast` and friends, so it's better to reduce
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the binary size.
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**Templates and template metaprogramming: Be careful and avoid if possible.**
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**Templates and template metaprogramming: Be careful and avoid if possible**
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- Rationale: Most templating results in code generation, and is one of the most
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common sources of binary bloat. Some use of templates is fine (e.g. an
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`ArrayRef<T>`, or code that handles multiple `ScalarType` types), but for the
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for basics.
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1. Push your branch to your fork of `pytorch/executorch`. Most people do not
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have permission to push a branch directoy to the upstream repo.
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have permission to push a branch directory to the upstream repo.
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1. Create your PR
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- Use the `main` branch as the base.
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- Give the PR a clear and descriptive title. It will become the title of the

README.md

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## Getting Started
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To get started you can:
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- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) on getting things running locally and deploy a model to a device
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- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) to get things running locally and deploy a model to a device
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- Use this [Colab Notebook](https://pytorch.org/executorch/stable/getting-started-setup.html#quick-setup-colab-jupyter-notebook-prototype) to start playing around right away
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- Jump straight into LLMs use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md)
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- Jump straight into LLM use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md)
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## Feedback and Engagement
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