Research on model-driven engineering has mainly focused on the use of models during software development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia to manage software complexity during development. Research on models at run-time seeks to extend the applicability of models and abstractions to the runtime environment, with the goal of providing effective technologies for managing the complexity of evolving software behaviour while it is executing. As is the case for many software development models, a runtime model is often created to support reasoning. However, in contrast to development models, runtime models are used to reason about the operating environment and runtime behaviour for some purpose, for example, to determine an appropriate form of runtime adaptation or reconfiguration. In this talk we will go trough some recent approaches for run-time models that support dynamic adaptation of systems with a particular emphasis on quantitative and probabilistic models. Beside describing these existing solutions, the talk also outlines a list of open challenges and shapes a potential future research agenda.