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

This project compresses a 100MB CSV file down to 7MB by overfitting a tiny 900KB transformer to memorize the file's exact byte patterns, then using arithmetic coding to encode the data based on the model's predictions. It is for researchers and engineers exploring neural network-based compression, particularly those interested in the limits of overfitting for data storage. The result is interesting because it demonstrates that a small, file-specific model can achieve significant compression ratios, challenging the assumption that generalizable models are always superior for data compression.