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ModelGalaxy: A Versatile Model Retrieval Platform

Published: 11 July 2024 Publication History

Abstract

With the growing number of available machine learning models and the emergence of model-sharing platforms, model reuse has become a significant approach to harnessing the power of artificial intelligence. One of the key issues to realizing model reuse resides in efficiently and accurately finding the target models that meet user needs from a model repository. However, the existing popular model-sharing platforms (e.g., Hugging Face) mainly support model retrieval based on model name matching and task filtering. If not familiar with the platform or specific models, users may suffer from low retrieval efficiency and a less user-friendly interaction experience. To address these issues, we have developed ModelGalaxy, a versatile model retrieval platform supporting multiple model retrieval methods, including keyword-based search, dataset-based search, and user-task-centric search. Moreover, ModelGalaxy leverages the power of large language models to provide users with easily retrieving and using models. Our source code is available at https://github.com/zwl906711886/ModelGalaxy.

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cover image ACM Conferences
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2024
3164 pages
ISBN:9798400704314
DOI:10.1145/3626772
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Published: 11 July 2024

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  1. large language model
  2. meta-learning
  3. model retrieval

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