There are not many places where 95% compression of UTF-8 plain text could outweigh needing a 30GB model in memory and a significant fraction of current LLM inference cost to decompress it − and good luck convincing librarians to adopt it.
The image versions are probably more useful though.
The important part for these codes is that it has pre-LLM functions for quality metrics to judge if the output is close enough to indistinguishable (preserves detail, doesn’t add any).
Although there is also variants deriving a neural net from the media to recreate it from the smaller model.
There are not many places where 95% compression of UTF-8 plain text could outweigh needing a 30GB model in memory and a significant fraction of current LLM inference cost to decompress it − and good luck convincing librarians to adopt it.
There’s a 300 MB library for audio compression using it. If you have large audio libraries it could eventually become worth the tradeoff.
huggingface.co/facebook/encodec_32khz
The image versions are probably more useful though.
The important part for these codes is that it has pre-LLM functions for quality metrics to judge if the output is close enough to indistinguishable (preserves detail, doesn’t add any).
Although there is also variants deriving a neural net from the media to recreate it from the smaller model.