In this paper we propose a general class of models for spreading processes we call the SI∗V ∗ model. Unlike many works that consider a fixed number of compartmental states, we allow an arbitrary number of states on arbitrary graphs with heterogeneous parameters for all nodes and edges. As a result, this generalizes an extremely large number of models studied in the literature including the MSEIV, MSEIR, MSEIS, SEIV, SEIR, SEIS, SIV, SIRS, SIR, and SIS models. Furthermore, we show how the SI∗V ∗ model allows us to model non-Poisson spreading processes letting us capture much more complicated dynamics than existing works such as information spreading through social networks or the delayed incubation period of a disease like Ebola. This is in contrast to the overwhelming majority of works in the literature that only consider dynamics that can be captured by Markov processes. After developing the stochastic model, we analyze its deterministic mean-field approximation and provide conditions for when the diseasefree equilibrium is stable. Simulations illustrate our results.