Language models have been effective in a wide range of applications, yet the
most sophisticated models are often proprietary. For example, GPT-4 by OpenAI
and various models by Anthropic are expensive and consume substantial energy.
In contrast, the open-source community has produced competitive models, like
Llama3. Furthermore, niche-specific smaller language models, such as those
tailored for legal, medical or financial tasks, have outperformed their
proprietary counterparts. This paper introduces a novel approach that employs
\textit{functional tokens} to integrate \textbf{multiple open-source models},
each optimized for particular tasks. Our newly developed Octopus v4 model
leverages \textit{functional tokens} to intelligently direct user queries to
the most appropriate vertical model and reformat the query to achieve the best
performance. Octopus v4, an evolution of the Octopus v1, v2, and v3 models,
excels in selection and parameter understanding and reformatting. Additionally,
we explore the use of graph as a versatile data structure that effectively
coordinates multiple open-source models by harnessing the capabilities of the
Octopus model and \textit{functional tokens}. Use our open-sourced GitHub
(\url{this https URL}) to try Octopus v4 models
(\url{this https URL}), and contrite to a larger
graph of language models. By activating models less than 10B parameters, we
achieved SOTA MMLU score of 74.8 among the same level models.

使用	extit {functional tokens} 和图作为数据结构，Octopus v4 模型整合多个开源模型，实现了更好的性能和选择参数的理解和重新格式化，达到了同级模型中最先进的 MMLU 得分 74.8。