Ooooh yeah sorry for the confusion.
As far as I know (I think I tried), you can use the torch.sparse tensors representations which will decompose a sparse tensor into its CSR format (location of non-zero values + these non-zero values). It should give you a MB compression gain.
The reason why I encoded the CSR format “by hand” is that sparse quantized tensors don’t exist yet in PyTorch so I had to do the quantization and the CSR format on top.