MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain
  Expertise
Chunyuan Deng Xiangru Tang Yilun Zhao Hanming Wang Haoran Wang Wangchunshu Zhou Arman Cohan Mark Gerstein
Recently, large language models (LLMs) have evolved into interactive agents,
proficient in planning, tool use, and task execution across a wide variety of
tasks. However, without specific agent tuning, open-source models like LLaMA
currently struggle to match the efficiency of GPT- 4, particularly given the
scarcity of agent-tuning datasets for fine-tuning. In response, we introduce
\textsc{Mimir}: a streamlined platform offering a customizable pipeline that
enables users to leverage both private knowledge and publicly available,
legally compliant datasets at scale for \textbf{personalized agent tuning}.
Additionally, \textsc{Mimir} supports the generation of general
instruction-tuning datasets from the same input. This dual capability ensures
that language agents developed through the platform possess both specific agent
abilities and general competencies. \textsc{Mimir} integrates these features
into a cohesive end-to-end platform, facilitating everything from the uploading
of personalized files to one-click agent fine-tuning.