ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation
Zihao Tang Zheqi Lv Shengyu Zhang Fei Wu Kun Kuang
The rapid advancement of Large Language Models (LLMs) has revolutionized
various sectors by automating routine tasks, marking a step toward the
realization of Artificial General Intelligence (AGI). However, they still
struggle to accommodate the diverse and specific needs of users and simplify
the utilization of AI models for the average user. In response, we propose
ModelGPT, a novel framework designed to determine and generate AI models
specifically tailored to the data or task descriptions provided by the user,
leveraging the capabilities of LLMs. Given user requirements, ModelGPT is able
to provide tailored models at most 270x faster than the previous paradigms
(e.g. all-parameter or LoRA finetuning). Comprehensive experiments on NLP, CV,
and Tabular datasets attest to the effectiveness of our framework in making AI
models more accessible and user-friendly. Our code is available at
https://github.com/IshiKura-a/ModelGPT.