We can extend our approach to grammar-based sampling, as explained in the paper linked above. Relevant PR: https://github.com/normal-computing/outlines/pull/178

Our method is much more efficient. llama.cpp loops over the entire vocabulary (~50k tokens) at each step to generate the mask. We generate an index at initialization, and building the masks at each step only requires a dictionary lookup (trade speed for memory). Sampling is just as fast as standard sampling.