WhiteLightning provides a variety of advanced configuration options to optimize model performance: 1) prompt optimization loops (-r parameter) improve data quality; 2) edge case generation feature (-generate-edge-cases) ensures the model's ability to handle complex inputs; 3) the ability to select different large language models as the data generator; 4) Adjustment of the volume of data per class via -target-volume-per-class; 5) Support for deployment methods such as Docker containers and GitHub Actions. These options allow developers to flexibly adjust the model training process according to specific needs.
This answer comes from the articleWhiteLightning: an open source tool for generating lightweight offline text classification models in one clickThe
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