Model persistence

[adsharma]

umap allows me to fit the model on a small sample of data and then persist the model to disk.

Subsequently, I can load the model from disk and compute hierarchy for a much larger number of samples that don't fit in memory.

Can I do the same with humap? Even if I can't persist to disk, being able to train on a small number of samples and compute low dimensional embedding (and presumably the same level1 and level2 embeddings) on a larger number of samples is super useful.