Hi,
Whenever you are dealing with machine learning models there is no specific rule to take the parameters, these parameters change according to dataset and the result you are expecting.
To answer your question if processing time and memory is not a constraint in your case you can use as many trees as possible. In general, the more trees you use the better results you get.
When it comes to the number of leaf nodes, you don’t want your model to overfit. Use Bias vs Variance trade-off in order to choose the number of leaf nodes wrt your dataset.
For implementation level information refer to the links:
