  Large Language Models~(LLMs) have demonstrated incredible capabilities in
understanding, generating, and manipulating languages. Through human-model
interactions, LLMs can automatically understand human-issued instructions and
output the expected contents, which can significantly increase working
efficiency. In various types of real-world demands, editing-oriented tasks
account for a considerable proportion, which involves an interactive process
that entails the continuous refinement of existing texts to meet specific
criteria. Due to the need for multi-round human-model interaction and the
generation of complicated editing tasks, there is an emergent need for
efficient general editing models. In this paper, we propose
\underline{\textbf{G}}eneral \underline{\textbf{SP}}arse
\underline{\textbf{E}}fficient \underline{\textbf{E}}diting
Mo\underline{\textbf{D}}el~(\textbf{G-SPEED}), which can fulfill diverse
editing requirements through a single model while maintaining low computational
costs. Specifically, we first propose a novel unsupervised text editing data
clustering algorithm to deal with the data scarcity problem. Subsequently, we
introduce a sparse editing model architecture to mitigate the inherently
limited learning capabilities of small language models. The experimental
outcomes indicate that G-SPEED, with its 508M parameters, can surpass LLMs
equipped with 175B parameters. Our code and model checkpoints are available at
\url{https://github.com/Banner-Z/G-SPEED}.
