Optimizing Large Language Model Inference Efficiency through Task Complexity Assessment

Introducing ComplexityNet, a framework that leverages fine-tuned smaller models to accurately assess task complexity and allocate tasks to the most appropriate Large Language Model, reducing computational resource usage by 90% while maintaining high code generation accuracy.