ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation
This work addresses the challenge of making AI models more accessible and user-friendly for average users, representing an incremental improvement in model generation efficiency.
The paper tackles the problem of LLMs struggling to meet diverse user needs and simplify AI model usage by proposing ModelGPT, a framework that generates tailored AI models from user descriptions, achieving up to 270x faster generation than previous methods.
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.