This repository provides a PyTorch implementation of Vision Mamba, a visual representation learning model based on bidirectional state space models (SSM). The core code in vision_mamba/model.py defines two classes: VisionEncoderMambaBlock, which applies a bidirectional SSM with convolutional processing, and Vim, which assembles a full vision classifier using patch embedding, a stack of encoder blocks, and an output head. The implementation relies heavily on the external `zetascale` library for the SSM primitive and `einops` for tensor rearrangements. Several issues indicate a work-in-progress state: the forward pass contains active print statements, a class token is defined but never used in the forward path, the convolutional layers use a kernel size of 1 (which may be incorrect for sequence modeling), and there are no unit tests or validation data. The abundant GitHub Actions workflows suggest an intent for continuous integration, but they reference non-existent test directories, so they would not pass. Overall, the repository is a functional but unpolished research prototype suitable for experimentation, but not production use or reuse as a library.