I'm using different models to model count data, the purpose of modelling is prediction. Values vary from 0 to 7. I try to use cross-validation method to assess out-of-sample predictive perfomance, but what error measure should I use? Is RMSE enough? What other methods of models comparing and assessment can I use?
RMSE is definitely the first thing that comes to mind. It punishes large deviations more than small ones, and its size is meaningful in terms of the underlying variable.
You can also define your own error function, if that is not suitable for your needs. Say, if small errors of 1-2 are not important when the baseline is large, you could define some sort of relative error (say $|y_i - \hat y_i|/y_i$).
Overall I would suggest you use a measure that is both familiar, and ideally the same measure you use to train your model. RMSE should fit both those criteria, unless you have a strong reason to prefer something else.