Training AI models has always been challenging, especially when there is a
need for custom models to provide personalized services. Algorithm engineers
often face a lengthy process to iteratively develop models tailored to specific
business requirements, making it even more difficult for non-experts. The quest
for high-quality and efficient model development, along with the emergence of
Large Language Model (LLM) Agents, has become a key focus in the industry.
Leveraging the powerful analytical, planning, and decision-making capabilities
of LLM, we propose a TrainerAgent system comprising a multi-agent framework
including Task, Data, Model and Server agents. These agents analyze
user-defined tasks, input data, and requirements (e.g., accuracy, speed),
optimizing them comprehensively from both data and model perspectives to obtain
satisfactory models, and finally deploy these models as online service.
Experimental evaluations on classical discriminative and generative tasks in
computer vision and natural language processing domains demonstrate that our
system consistently produces models that meet the desired criteria.
Furthermore, the system exhibits the ability to critically identify and reject
unattainable tasks, such as fantastical scenarios or unethical requests,
ensuring robustness and safety. This research presents a significant
advancement in achieving desired models with increased efficiency and quality
as compared to traditional model development, facilitated by the integration of
LLM-powered analysis, decision-making, and execution capabilities, as well as
the collaboration among four agents. We anticipate that our work will
contribute to the advancement of research on TrainerAgent in both academic and
industry communities, potentially establishing it as a new paradigm for model
development in the field of AI.

利用强大的分析、规划和决策能力，以及四个代理的协作，我们提出了一种 TrainerAgent 系统，它由任务、数据、模型和服务器代理组成，通过综合地从数据和模型的角度对用户定义的任务、输入数据和需求进行优化，从而获得满足要求的模型，并将这些模型作为在线服务部署，相较于传统模型开发，我们的系统以增加效率和质量的方式取得了显著进展。