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When we export models with AOT Inductor for servers and desktops, and Executorch for mobile and edge devices,
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When we export models with AOT Inductor for servers and desktops, and ExecuTorch for mobile and edge devices,
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we will save them in the specified directory (`${MODEL_OUT}` in our example below) as a DSO under the name `${MODEL_NAME}.so` (for AOTI-generated dynamic libraries),
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or as Executorch model under the name `${MODEL_NAME}.pte` (for Executorch-generated mobile/edge models).
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or as ExecuTorch model under the name `${MODEL_NAME}.pte` (for Executorch-generated mobile/edge models).
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We use `[ optional input ]` to indicate optional inputs, and `[ choice 1 | choice 2 | ... ]` to indicate a choice
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@@ -271,7 +271,7 @@ quantization to achieve this, as described below.
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We export the model with the export.py script. Running this script requires you first install executorch with pybindings, see [here](#setting-up-executorch-and-runner-et).
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At present, when exporting a model, the export command always uses the
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xnnpack delegate to export. (Future versions of torchchat will support additional
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delegates such as Vulkan, CoreML, MPS, HTP in addition to Xnnpack as they are released for Executorch.)
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delegates such as Vulkan, CoreML, MPS, HTP in addition to Xnnpack as they are released for ExecuTorch.)
@@ -607,7 +607,7 @@ After this is done, you can run runner-et with
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```
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While we have shown the export and execution of a small model to a mobile/edge
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device supported by Executorch, most models need to be compressed to
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device supported by ExecuTorch, most models need to be compressed to
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fit in the target device's memory. We use quantization to achieve this.
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@@ -630,7 +630,7 @@ To run your pte model, use the following command (assuming you already generated
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### Android
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Check out the [tutorial on how to build an Android app running your PyTorch models with Executorch](https://pytorch.org/executorch/main/llm/llama-demo-android.html), and give your torchchat models a spin.
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Check out the [tutorial on how to build an Android app running your PyTorch models with ExecuTorch](https://pytorch.org/executorch/main/llm/llama-demo-android.html), and give your torchchat models a spin.
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