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…
In plain words
This experiment demonstrates a novel compression technique that trains a small 900KB transformer model to memorize individual files rather than generalize across data. The transformer predicts each successive byte, with predictions fed into an arithmetic coder to produce compressed output. On test data, it achieves high compression ratios: a 100MB CSV compresses to 7MB, and text data to 21MB. The process is computationally intensive, requiring 20-30 minutes of training plus 45 minutes each for compression and decompression. It is intended for developers exploring machine learning-based compression methods.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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
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