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LLMs are surprisingly great at compressing images and audio, DeepMind researchers find
(venturebeat.com)
this post was submitted on 27 Sep 2023
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Say, if you compress some data using these LLMs, how hard it is to decompress the data again without access to the LLM used to perform the compression? Is the compression "algorithm" used by the LLM will be the same for all runs (which means you probably can reverse engineer it to created a decompressor program), or will it be different every time it compress new data?
I mean, having to download a huge LLM to decompress some data, which probably also requires GPU with big VRAM, seems a bit much.
Skimming through the linked paper, I noticed this:
So it sounds like the model parameters needed to decompress the file are included in the file itself.
So, you'll have to use the same LLM to decompress the data? For example, if your friend send you an archive compressed with this LLM, then you won't be able to decompress it without downloading the same LLM?
This is not dissimilar to regular compression algorithms. If I compress a folder using the 7zip format (.7z) the end user needs to use 7zip to decompress it since it is a proprietary algorithm. (I know Windows 11 is getting 7zip support)
Except LLMs tend to be very big compared to standard decompression programs and often requires GPU with adequate VRAM in order to work reasonably fast enough. This is a very big usability issue IMO. If decompression can be done with a smaller and faster program (maybe also generated by the LLM?), it can be very useful and see pretty wide adoption (e.g. for future game devs who want to reduce their game size from 150GB to 130GB).
Training tends to be more compute intensive while inference is more likely to be able to be ran on a smaller hardware foot print.
The neater idea would be a standard model or set of models, so that a 30G program can be used on ~80% of target case, games and video seem good canidates for this.
I don't know how this would apply to decompression models in actuality, but in general, deep learning is VRAM intensive only during the training process, that's because they train multiple batches of data at once for generalization, and all those batches of data need to be stored in ram.
But once the model is trained, the end user is only going to input data one by one, so VRAM usually is not an issue. There are also light weight models that are designed to be run on lower end hardware.