DNN model reuse provides an efficient way to meet a new requirement without training a model from scratch, which is becoming more feasible, particularly with the massive models available on sharing platforms (e.g., HuggingFace). Recently, on-demand model reuse has drawn much attention, which aims to reduce the overhead and security risk of model reuse via decomposing models into modules and reusing modules according to user’s requirements. However, existing efforts for on-demand model reuse mainly provide algorithm implementations without tool support. These implementations involve ad-hoc decomposition in experiments and require considerable manual effort to adapt to new models; thus obstructing the practicality of on-demand model reuse. In this paper, we introduce \projectName, a tool that systematically integrates two modularization approaches proposed in our prior work. \projectName provides automated and scalable model decomposition and module reuse functionalities, making it more practical and easily integrated into model-sharing platforms. Evaluations conducted on widely used models sourced from PyTorch and GitHub platforms demonstrate that \projectName achieves effective model decomposition and module reuse, as well as scalability to various models. A demonstration is available at https://youtu.be/dXHeQ0fGldk.