fivetech/MiMo-V2.5-colibri-peng-int4-zstd

MiMo-V2.5 311B — int4 + lossless zstd container for peng

109 GB instead of 152 GB (71.8%), bit-exact same model as fivetech/MiMo-V2.5-colibri-peng-int4.

Every tensor in the safetensors shards is stored as one zstd-1 frame (__metadata__.peng_zstd="1", per-tensor "nb" = uncompressed size). The peng engine (commit 6ffb8d9+) reads this format natively and produces bit-exact output vs the uncompressed container (verified: per-frame byte-compare of all 17 shards + identical TEMP=0 generations at full scale).

Which container should you download?

your setup	use
limited bandwidth / storage	this one (26% smaller)
plenty of RAM (experts pinned once at boot)	this one — decompress cost is paid once at load
NVMe-streaming on a core-limited box, max tok/s	the plain int4 container — measured ~18% faster streaming on a 16-core WSL2 box, because zstd decompression competes with expert matmul for cores

Honest measurements, including the failed speed experiments: findings.md §41.

Run

git clone https://github.com/fivetechsoft/peng-mimo && cd peng-mimo/c && make CUDA=1 mimo
SNAP=/path/to/this/repo COLI_CUDA=1 CUDA_DENSE=1 DIRECT=1 TAO=1 \
  PROMPT='Write one short sentence about Rome.' NGEN=24 ./mimo 64 4 8

To convert back to the plain int4 layout (or repack your own container): c/tools/repack_zstd.py (--verify byte-compares every frame).

Derived from colibri (Apache 2.0, © JustVugg).