export

To export a PyTorch model for edge inference, use the export function with example inputs to generate a portable `.pte` file suitable for on-device deployment. This process requires minimal manual configuration and supports various model architectures.

Model lowering optimizes and converts PyTorch architectures into a portable edge format. You need model lowering when preparing models for on-device AI applications to ensure efficient execution without server dependence.

Yes, you can export specific architectures like Llama or Whisper by integrating with provided model-specific scripts. The conversion supports various model architectures to generate optimized edge-ready formats.

PyTorch model conversion to `.pte` format ensures models are optimized and lowered with minimal manual configuration. You simply execute export functions and model-specific scripts to generate the portable edge file.

The best way to prepare a language model for mobile deployment is exporting the evaluated PyTorch model using dedicated scripts. This generates a portable `.pte` file optimized for edge devices, ensuring efficient on-device inference.