  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.
