MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise
arxiv(2024)
摘要
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
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 personalized agent tuning.
Additionally, 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. 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.
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