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    You are at:Home»Technology»Fabrice Bellard’s TS Zip (2024)
    Technology

    Fabrice Bellard’s TS Zip (2024)

    TechAiVerseBy TechAiVerseJanuary 13, 2026No Comments2 Mins Read2 Views
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    Fabrice Bellard’s TS Zip (2024)

    The ts_zip utility can
    compress (and hopefully decompress) text files using a Large Language
    Model. The compression ratio is much higher than with other
    compression tools. There are some caveats of course:

    • A GPU is necessary to get a reasonable speed. 4 GB of RAM is
      required.
    • It is slower than conventional compressors (compression and
      decompression speed: up to 1 MB/s on a RTX 4090).
    • Only text files are supported. Binary files won’t be compressed
      much. The currently used language model (RWKV 169M v4) was trained
      mostly on English texts. Other languages are supported including
      source code.
    • It is experimental so no backward compability should be expected
      between the various versions.
    • See also ts_sms which is optimized for the
      compression of small messages.

    Compression Ratio

    The compression ratio is given in bits per byte (bpb).

    File Original size
    (bytes)
    xz
    (bytes) (bpb)
    ts_zip
    (bytes) (bpb)
    alice29.txt 152089 48492 2.551 21713 1.142
    book1 768771 261116 2.717 137477 1.431
    enwik8 100000000 24865244 1.989 13825741 1.106
    enwik9 1000000000 213370900 1.707 135443237 1.084
    linux-1.2.13.tar 9379840 1689468 1.441 1196859 1.021

    Results and speed for other programs on enwik8 and enwik9 are
    available at the Large
    Text Compression Benchmark
    .

    Download

    • Linux version: ts_zip-2024-03-02.tar.gz.
    • Windows version: ts_zip-2024-03-02-win64.zip.

    Technical information

    • ts_zip uses
      the RWKV 169M v4
      language model which is a good compromise between speed and
      compression ratio. The model is quantized to 8 bits per parameter
      and evaluated using BF16 floating point numbers.
    • The language model predicts the probabilities of the next token. An
      arithmetic coder then encodes the next token according to the
      probabilities.
    • The model is evaluated in a deterministic and reproducible
      way. Hence the result does not depend on the exact GPU or CPU
      model nor on the number of configured threads. This key point
      ensures that a compressed file can be decompressed using a
      different hardware or software configuration.

    Fabrice Bellard – https://bellard.org/

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