Spinal pathologies are common and include trauma, malignancy, infection, and degenerative disease. Adult spine deformity (ASD) is a consequence of one or several of these pathologies, and it stands for a sagittal misalignment of the vertebral column. Computational approaches, like Finite Element (FE) Models have been proposed as effective tools to explore the aetiology or the treatment of ASD, through biomechanical simulations. However, while the personalization of the models is a cornerstone, personalized FE models are cumbersome to generate. To cover this need, we share a repository of FE models with different spine morphologies that stand for a statistical representation of real geometries from a patient cohort. To generate these models, low-dose bi-planar EOS images are used and 3D surface spine models are reconstructed. Then, a Statistical Shape Model (SSM), is built and is used to adapt a FE structured mesh template for both the bone and the soft tissues of the spine, using mesh morphing. Then, the SSM deformation fields allows the personalization of the mean structured FE model, based on sagittal balance measurements. The new structured SSM tool is eventually proposed to generate a virtual cohort of 16807 thoracolumbar FE spine models, shared in this public repository. Mesh quality and predicted ranges of motion have been assessed to evaluate the quality of the FE models and calculations.