In recent years, ML-based heuristic functions for automated planning have shown increasing performance. A main challenge is the level of generalization required in planning: techniques must generalize at least across different instances of the same domain (which results in different sizes of learning input). A common approach to overcome the issue is to use graph representations as input. While GNNs are a natural choice for learning, other methods have recently been favored because they show better runtime performance and need less training data. However, existing work has so far been limited to non-hierarchical planning. We describe the first approach to learn heuristics for hierarchical planning. We extend the Instance Learning Graph – a graph structure used in non-hierarchical planning – to the new setting and show how to learn heuristic functions based on it. Since our heuristics are applicable to the lifted model, there is no need to ground it. We therefore combine it with a novel lifted HTN planning system. Like recent systems in non-hierarchical planning, it grounds the search space explored so far, but not the entire model prior to search. Our evaluation shows that our approach is competitive with the lifted systems from the literature, though the ground systems achieve higher coverage.