SeedPack

Lossless GPU archiver for GGUF model repositories. A whole repository, the BF16 model and all its quants from IQ1 to Q8, packs into one archive about the size of a single compressed BF16 file. Every file comes back byte for byte.

The archive is smaller than the repository's largest single quant (UD-Q8_K_XL, 844 MB). The full benchmark against 10 other compressors, with speeds and single files, is on the example archive's page.

Models bigger than the GPU's memory are streamed: all of unsloth/Qwen3.5-9B-GGUF (BF16, mmproj and 23 quants, 147.8 GB) packs into 12.4 GB in under 4 minutes on a 12 GB card, and its Q4_K_M comes back in 11–16 s.

Install

pip install seedpack

Requirements:

• An NVIDIA GPU, Turing (RTX 20xx) or newer, with a CUDA 13 driver (R580 or newer).
• Python 3.11+.
• Windows 10/11 or Linux. Tested on an RTX 5070 under Windows 10 (Python 3.11) and Ubuntu on WSL2 (Python 3.14).
• No CUDA Toolkit is needed: NVRTC, nvJitLink and the CUDA headers come from pip.

Archives are portable between operating systems: an archive packed on Windows unpacks bit-exactly on Linux, and the other way round. The first run compiles the GPU quantizers for your card (about 15 s) and caches them in %LOCALAPPDATA%\seedpack\cache or ~/.cache/seedpack; set SEEDPACK_CACHE to move the cache.

Use

# straight from the Hugging Face Hub: download once, extract what you need
seedpack list hf://neurosquad/Qwen3-0.6B-SEEDPACK
seedpack x    hf://neurosquad/Qwen3-0.6B-SEEDPACK Qwen3-0.6B-Q4_K_M.gguf -o models
seedpack x    hf://neurosquad/Qwen3-0.6B-SEEDPACK "*Q4_K*" "*Q8_0*" -o models
seedpack x    hf://neurosquad/Qwen3-0.6B-SEEDPACK -o models      # everything

# local archives
seedpack pack  models/Qwen3-0.6B-GGUF -o qwen3.seedpack           # files and/or folders
seedpack x     qwen3.seedpack Qwen3-0.6B-Q4_K_M.gguf -o out
seedpack test  qwen3.seedpack                                         # decode and verify, write nothing

Notes on the commands:

• hf://OWNER/REPO[/FILE.seedpack][@REVISION] and https://huggingface.co/... links work wherever an archive is expected.
• list reads only the archive's index over the network; x and test download the archive once into the Hugging Face cache.
• Extracted files are checked against BLAKE3 hashes. Every chunk of the archive carries a CRC32.

How it works

A GGUF repository ships the same weights 20–30 times: the BF16 model plus each quantization of it. To a general-purpose compressor that looks like gigabytes of almost incompressible bytes. SeedPack uses the fact that every quant is a deterministic function of the same weights:

• The seed is stored once. The most precise model (the BF16 file, or the senior quant when there is none) is coded with an entropy coder built for its weights.
• Quants are re-derived, not stored. Each quant is computed again on the GPU by a bit-exact CUDA port of llama.cpp's quantizers (ggml-quants.c, compiled without FMA contraction), with the repository's importance matrix.
• Only the residual is kept. Where a re-derived quant differs from the published file, the residual is coded with the quantizer's own probabilities: scales first, then each code relative to its rounding boundary. For Qwen3-0.6B, all 25 quants together cost about 1 MB on top of the seed.
• Duplicates are stored once. Identical tensors in different files are kept once. Files that are not GGUF are compressed with LZMA or zstd.

Links

• Source and research notes: https://github.com/glmn-ai/seedpack
• Example archives: https://huggingface.co/neurosquad/Qwen3-0.6B-SEEDPACK, https://huggingface.co/neurosquad/Qwen3.5-9B-SEEDPACK
• A NeuroSquad project.

License

Apache-2.0. The bundled llama.cpp / ggml quantization sources (seedpack/ggml/) are MIT-licensed; see NOTICE.