N-grams in NLP deep learning
Theoretically, you could use n-grams to model text sequences. But there are some good reasons why it's not often mentioned in textbooks.

Intuitively, modeling sequences is learning that after a, b, and c comes d. So if your sequence is:

"Hello, my name is Bob, what a lovely day today, how are you?"

It technically doesn't matter if a = "Hello", or "H", or "Hello, my name is Bob". To your model, it will just learn that after a thing, comes another thing etc.

So yes, you could use n-grams. But the problem is that you need to make sure that the way the ngrams are split is meaningful (i.e. "Hello, my" is a probably worse split than "my name is", which probably happens more often)

But don't worry, the whole point of using LSTM is that the memory part of the network will mimic using n-grams, as long as it helps with the overall performance. This means that if it needs to take more words into account to predict the next one, it will do it up to the depth of your network.