Show HN: Overfitted a 900KB Transformer to Compress a 100MB CSV into 7MB

I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files.

Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output.

On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte).

It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my AMD 7800XT).

Checkout the repo - https://github.com/samyak112/pym-particles

I tried it on a enwik9 100 mb slice and was able to compress it to 20 mb + 900kb transformer so 21mb.

I know the top submission was able to get it to 13 mb.

Still trying some ideas to get better compression.

That’s so awesome! I want to try something similar. I’ve been going crazy with compression work. I reckon I can beat that prize link

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For the model overhead to become significant enough to eat into the gains, the file size would need to be fairly small, right? I assumed nobody would use this for compressing anything below 100 MB.

I tested with 100 MB files because anything larger takes a long time to evaluate. The actual target was at least 1 GB, and in that case I would use a 100 MB model (Shannon entropy rules).

I also tried it on a 100 MB Photoshop file and was able to compress it down to 45 MB, whereas ZIP could only get it down to 60 MB. So yeah still not losing gains.