This paper introduces Deform Any Liver (DAL), a realtime surrogate model designed to accurately predict any liver deformation, given a set of applied forces. DAL leverages a hypernetwork to condition a U-Net architecture on the liver shape, with the U-Net receiving the applied external forces as input to predict the resulting displacement field. This design enables the model to generalize across various patient geometries, overcoming the limitations of existing patient-specific approaches. The DAL model is trained, validated, and tested using real human liver geometries, employing finite element method (FEM) simulations to generate realistic deformation data. Our results demonstrate the model's effectiveness and adaptability in accurately predicting the deformation of various liver shapes.