Generalist reconstruction foundation model for accelerating cardiac MRI (CMR) acquisition, designed to recover diagnostic-quality images from highly undersampled (8x-24x) multi-coil k-space data across heterogeneous scanners, field strengths, and cardiovascular diseases. Combines a CLIP-ViT-based module for semantic/contextual understanding of the anatomy being imaged with a physics-informed data-consistency reconstruction network, trained on MMCMR-427K -- the largest public multimodal CMR k-space database to date. Intended as an upstream substrate that feeds downstream segmentation, phenotyping, and diagnosis models (e.g. automated cardiac-phenotype extraction via nnU-Net) rather than replacing them. Released by the CMRxRecon-challenge consortium; code and the underlying database are public for academic, non-commercial use, but no separately downloadable pretrained checkpoint is provided.