LiteRT

LiteRT (previously known as TensorFlow Lite) is a high-performance runtime designed for on-device machine learning.

The Optimum library exports a model to LiteRT for many architectures.

The benefits of exporting to LiteRT include the following.

• Low-latency, privacy-focused, no internet connectivity required, and reduced model size and power consumption for on-device machine learning.
• Broad platform, model framework, and language support.
• Hardware acceleration for GPUs and Apple Silicon.

Export a Transformers model to LiteRT with the Optimum CLI.

Run the command below to install Optimum and the exporters module for LiteRT.

pip install optimum[exporters-tf]

optimum-cli export tflite --help

Set the --model argument to export a from the Hub.

optimum-cli export tflite --model google-bert/bert-base-uncased --sequence_length 128 bert_tflite/

You should see logs indicating the progress and showing where the resulting model.tflite is saved.

Validating TFLite model...
	-[✓] TFLite model output names match reference model (logits)
	- Validating TFLite Model output "logits":
		-[✓] (1, 128, 30522) matches (1, 128, 30522)
		-[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05)
The TensorFlow Lite export succeeded with the warning: The maximum absolute difference between the output of the reference model and the TFLite exported model is not within the set tolerance 1e-05:
- logits: max diff = 5.817413330078125e-05.
 The exported model was saved at: bert_tflite

For local models, make sure the model weights and tokenizer files are saved in the same directory, for example local_path. Pass the directory to the --model argument and use --task to indicate the task a model can perform. If --task isn’t provided, the model architecture without a task-specific head is used.

optimum-cli export tflite --model local_path --task question-answering google-bert/bert-base-uncased --sequence_length 128 bert_tflite/