ts_zip: Text Compression using Large Language Models

The article discusses the `ts_zip` utility, a tool provided with the ts\_server software, which can compress and decompress text files with Large Language Models. The utility offers a higher compression ratio than other tools, but it requires the language model to be available during decompression, a GPU for reasonable speed, and the same GPU model and program versions for both compression and decompression. The model is also frozen, meaning it only works for text files in a language the model has already encountered.

The article provides data on the compression ratio and speed, as well as the memory required for different models. The data indicates that the smaller RWKV models are a good compromise for text compression due to their RNN structure, which requires less memory and offers a relatively high running speed. The author concludes that these models are a good choice for text compression tasks.

Key takeaways:

The `ts_zip` utility can compress text files with Large Language Models at a higher compression ratio than other tools, but requires the same language model and GPU for decompression.
The compression ratio varies depending on the model and file, with the best results shown for the `rwkv_7B_q4` model on the `alice29.txt` file.
Compression speed and memory requirements are also dependent on the model, with the `rwkv_169M` model showing the fastest speed and least memory usage on the `book1` file.
The smaller RWKV models are recommended for text compression due to their balance of memory usage and running speed.

ts_zip: Text Compression using Large Language Models

Key takeaways:

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