# Green Runner: A tool for efficient model selection from model repositories

arXiv: https://arxiv.org/abs/2305.16849 · HTML: https://mlsanity.com/paper/2305.16849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 4 of 4)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Which Model to Transfer? Finding the Needle in the Growing Haystack](https://mlsanity.com/paper/2010.06402/) (2020)
- [Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash](https://mlsanity.com/paper/1805.11815/) (2018)

## Built on by (most cited first, 1 of 1)

- [Prompt-based Code Completion via Multi-Retrieval Augmented Generation](https://mlsanity.com/paper/2405.07530/) (2024)

## Abstract (arXiv)

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.