WhiteLightning utilizes the ONNX format and multiple adapters to ensure deployment compatibility:
- unified runtimeUnified export of all models to ONNX format (less than 1MB), supports ONNX Runtime for cross-platform operation.
- Multi-language support: Provide examples for Python, Rust, Swift, Node.js, Dart, etc. (e.g. Python needs to be installed).
onnxruntimepackage) - hardware adaptation: Provides quantization options for devices such as the Raspberry Pi, which can be deployed via Docker containers to ensure environment consistency.
- Offline verification: Use prior to deployment
ort.InferenceSessionLoad model tests to ensure input and output formats match
If you are still experiencing problems, you can check if the device architecture supports ONNX Runtime, or get device-specific deployment guides through the GitHub community.
This answer comes from the articleWhiteLightning: an open source tool for generating lightweight offline text classification models in one clickThe
May not be reproduced without permission:AI productivity tools " How do I address compatibility issues with WhiteLightning models when deployed on edge devices?
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