Recently, Cartis, Gould, and Toint have introduced an adaptive cubic with regularization for minimizing unconstrained optimization problems with smooth non-convex objective functions. Inspired by their work and using a smoothing approach, an adaptive cubic with regularization method for minimizing an unconstrained optimization problem with locally Lipschitz objective function is introduced. The global convergence and worst-case complexity of the introduced method will also be discussed.