Stochastic model-based methods have received increasing attention lately due to their appealing robustness to the stepsize selection and provable efficiency guarantee for non-smooth non-convex optimization. To further improve the performance of stochastic model-based methods, we make two important extensions. First, we propose a new minibatch algorithm which takes a set of samples to approximate the model function in each iteration. For the first time, we show that stochastic algorithms achieve linear speedup over the batch size even for non-smooth and non-convex problems. To this end, we develop a novel sensitivity analysis of the proximal mapping involved in each algorithm iteration. Our analysis can be of independent interests in more general settings. Second, motivated by the success of momentum techniques for convex optimization, we propose a new stochastic extrapolated model-based method to possibly improve the convergence in the non-smooth and non-convex setting. We obtain complexity guarantees for a fairly flexible range of extrapolation term. In addition, we conduct experiments to show the empirical advantage of our proposed methods.