Current methods in Federated and Decentralized learning presume that all clients share the same model architecture, assuming model homogeneity. However, in practice, this assumption may not always hold due to hardware differences. While prior research has addressed model heterogeneity in Federated Learning, it remains unexplored in fully decentralized or peer-to-peer settings. Therefore, in this paper, we investigate a real-world yet challenging situation involving model heterogeneity in a fully decentralized context. Furthermore, we introduced a Model Agnostic Peer-to-peer Learning (MAPL) framework, which allows simultaneous learning of heterogeneous personalized models. Additionally, we define a graph learning objective to infer optimal collaboration weights based on task similarity. Experiments reveal that even in this challenging scenario, MAPL delivers competitive results while being communication efficient owing to the sparse collaboration graph in both model homogeneous and heterogeneous settings.