# Prepare a LiteRT model

You can use an existing LiteRT model by downloading it from the
open-source community or convert a TensorFlow model or a Keras model
to the LiteRT format using specific tools. After converting the
model, you can create an application and run inference on a device,
and develop a custom application for the LiteRT model.

The following figure shows you can either use pre-optimized
models or convert models to LiteRT models using Python APIs and then
quantize them to run inference. You can also use the `tflite_convert`
command for basic model conversion.

![../_images/litert-model-usage-workflow.png](data:image/png;base64,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)

**LiteRT model usage workflow**

Next steps

- [Use a pre-optimized LiteRT model](https://docs.qualcomm.com/doc/80-80022-15B/topic/use-preoptimized-litert-model.html)
- [Export a TensorFlow model to LiteRT](https://docs.qualcomm.com/doc/80-80022-15B/topic/export-tf-model-litert.html)
- [Export ONNX model to a LiteRT](https://docs.qualcomm.com/doc/80-80022-15B/topic/export-onnx-model-to-litert.html)
- [Export a Pytorch model to LiteRT](https://docs.qualcomm.com/doc/80-80022-15B/topic/export-pytorch-model-litert.html)

Last Published: May 14, 2026

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