ztensor

Read any tensor format, write canonical .zt.

pip install ztensor

import ztensor, torch

with ztensor.open("model.safetensors") as src:   # or open([shard1, shard2])
    for t in src.values():                       # src itself is a mapping,
        print(t.name, t.shape, t.dtype)          # so iterating it gives names

    t = src["model.layers.0.mlp.w"]
    w = torch.from_dlpack(t)      # zero-copy
    t.location                    # (path, offset, nbytes) for your own I/O
    t["scales"]                   # parts are tensors too

A tensor with one part answers about that part. A tensor with several, a quantized one or a CSR one, does not pick for you: index the part you mean.

Reads .zt, .safetensors, .gguf, .npz, PyTorch .pt, HDF5 and ONNX, detected by magic. Writes exactly one format: canonical .zt, where every tensor starts on a 64 KiB page and carries an XXH3 digest.

No framework modules

Tensors export DLPack and the buffer protocol, so numpy, torch and jax read them zero-copy without this package knowing any of them exist:

numpy.from_dlpack(t)
torch.from_dlpack(t)
memoryview(t)

DLPack also carries bfloat16, which the numpy dtype table cannot, so the dtype most checkpoints are actually in survives the trip.

A zero-copy export keeps the mapping alive on its own: an array stays valid after the source it came from is closed.

It tells you what it can do

t.caps.map       # a zero-copy export will work
t.caps.locate    # .location gives the exact byte range
t.caps.evict     # its pages are its own
t.caps.verify    # there is a digest to check

Each is the precondition of the operation it names, so the report cannot disagree with the behaviour. When only metadata is wanted, ztensor.index() opens without mapping: names, shapes and addresses, for the cost of a header read.

Converting

ztensor.convert("model.safetensors", "model.zt")
ztensor.verify("model.zt", deep=True)      # (digest_verified, without_digests)

Conversion is the upgrade path: the foreign file has no digests and arbitrary alignment; the result has both.

The numpy shim

ztensor.numpy.load_file / save_file are a safetensors-shaped convenience layer, kept for code written against that API. New code should use the tensor handles above, which can say things a dict of arrays cannot.

Linux and macOS. MIT licensed. The format, the Rust crates and the specification live at github.com/pie-project/ztensor.

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Credits

Author: In Gim

GitHub Statistics

Data verified by PyPI on Aug 4, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.9

Provides Extra

numpy

Tags

tensor safetensors gguf checkpoint dlpack mmap

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This release

2.1.2 This release

21 files

2.1.1

21 files

2.1.0

21 files

2.0.0

21 files

1.2.3

34 files

1.2.2

34 files

1.2.1

34 files

1.2.0

34 files

1.1.1

14 files

1.0.2

14 files

1.0.1

14 files

1.0.0

14 files

0.1.4

15 files

0.1.2

15 files

0.1.1

15 files

0.1.0

15 files

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