You can convert TensorFlow, PyTorch and JAX models to the TFLite format.
This can be done using AI Edge conversion tools.
LiteRT rises to various ODML (On-Device Machine Learning) challenges:
1. Connectivity - ability to execute without an Internet connection
2. Size - reduced model and binary size
3. Privacy/data restrictions - no personal data leaves the device
4. Power consumption - efficient inference and a lack of network connections
Operationally, LiteRT models use an efficient portable format known as FlatBuffers, and the .tflite file extension. (See here for the difference between FlatBuffers and protobuf).
