J.-T. Ma

Knowledge Distillation Method for Compressing Large Language Model of Power Risk Identification and Improving Deployment Efficiency

Experiments show that, after applying the proposed knowledge distillation method, the inference latency is reduced from 235 ms to a minimum of 26 ms, which is better than DistilBERT’s 35 ms, verifying the efficiency and practicality of the lightweight model in resource-constrained scenarios involving power-risk identification, electromagnetic sensing, and edge-based intelligent monitoring.