Green Runner: A tool for efficient model selection from model
  repositories
Jai Kannan Scott Barnett Anj Simmons Taylan Selvi Luis Cruz
Deep learning models have become essential in software engineering, enabling
intelligent features like image captioning and document generation. However,
their popularity raises concerns about environmental impact and inefficient
model selection. This paper introduces GreenRunnerGPT, a novel tool for
efficiently selecting deep learning models based on specific use cases. It
employs a large language model to suggest weights for quality indicators,
optimizing resource utilization. The tool utilizes a multi-armed bandit
framework to evaluate models against target datasets, considering tradeoffs. We
demonstrate that GreenRunnerGPT is able to identify a model suited to a target
use case without wasteful computations that would occur under a brute-force
approach to model selection.