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antibody_design_trunk.pth
iggm
weights
101,650,827
87b4399a9d821da49c2546a9c78d5dd89a071a0eb5085e78ae5455ef82146967
[ "https://hf-mirror.com/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/antibody_design_trunk.pth", "https://huggingface.co/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/antibody_design_trunk.pth" ]
[ "iggm" ]
mcp/runner/download_models_worker.py
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/antibody_design_trunk.pth
MIT
IgGM authors
antibody_fr_design_trunk.pth
iggm
weights
101,633,395
dfb95c6dbabd9dbb8be197d507ec4452e44fcbb5f284181164c48024c228c8a7
[ "https://hf-mirror.com/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/antibody_fr_design_trunk.pth", "https://huggingface.co/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/antibody_fr_design_trunk.pth" ]
[ "iggm" ]
mcp/runner/download_models_worker.py
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/antibody_fr_design_trunk.pth
MIT
IgGM authors
antibody_inverse_design_trunk.pth
iggm
weights
101,136,279
3543ba001657c71b29d1150973d4b1f9fc490e74f67eb98754f90e8ae9113a8b
[ "https://hf-mirror.com/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/antibody_inverse_design_trunk.pth", "https://huggingface.co/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/antibody_inverse_design_trunk.pth" ]
[ "iggm" ]
mcp/runner/download_models_worker.py
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/antibody_inverse_design_trunk.pth
MIT
IgGM authors
downloads/iggm-06abc563b3fc8c7ea020543add16b69b6f8a1c8d.tar.gz
iggm
source-archive
null
cc0dadaca30718618015e912c178a9cb6f5d811576418e98548087234665671e
[ "https://gh-proxy.com/https://codeload.github.com/TencentAI4S/IgGM/tar.gz/06abc563b3fc8c7ea020543add16b69b6f8a1c8d", "https://codeload.github.com/TencentAI4S/IgGM/tar.gz/06abc563b3fc8c7ea020543add16b69b6f8a1c8d" ]
[ "iggm" ]
mcp/runner/setup_runtime.py
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/downloads/iggm-06abc563b3fc8c7ea020543add16b69b6f8a1c8d.tar.gz
AFL-3.0
Tencent AI4S IgGM authors
esm_ppi_650m_ab.pth
iggm
weights
2,604,382,144
c8762add3fad57aff561aeff20765493c2ae3b6789ea060a759c8c3f0c0f6fca
[ "https://hf-mirror.com/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/esm_ppi_650m_ab.pth", "https://huggingface.co/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/esm_ppi_650m_ab.pth" ]
[ "iggm" ]
mcp/runner/download_models_worker.py
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/esm_ppi_650m_ab.pth
MIT
IgGM authors
igso3_buffer.pth
iggm
weights
371,429,898
7ca1a99c22f7a2770e41c5b310e95c289a2bf1eb27d39a221176dc4332cedbcf
[ "https://hf-mirror.com/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/igso3_buffer.pth", "https://huggingface.co/Wolkenwandler/IgGM/resolve/a7175bad02c0547223580ada3442f369a122d043/igso3_buffer.pth" ]
[ "iggm" ]
mcp/runner/download_models_worker.py
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/igso3_buffer.pth
MIT
IgGM authors
stereo_chemical_props.txt
iggm
other
9,119
24510899eeb49167cffedec8fa45363a4d08279c0c637a403b452f7d0ac09451
[ "https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt" ]
[ "iggm" ]
mcp/runner/download_models_worker.py
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/stereo_chemical_props.txt
PD-academic
Oldfield et al. stereochemical parameters (academic public data)
wheels/torch-2.7.1+cu128-cp311-cp311-manylinux_2_28_x86_64.whl
iggm
wheel
1,039,389,795
c301dc280458afd95450af794924c98fe07522dd148ff384739b810e3e3179f2
[ "https://download.pytorch.org/whl/cu128/torch-2.7.1+cu128-cp311-cp311-manylinux_2_28_x86_64.whl" ]
[ "iggm" ]
.levin-plugin/plugin.json (python 3.11); torch==2.7.1+cu128
https://huggingface.co/datasets/LevinHarness/iggm-assets/resolve/main/wheels/torch-2.7.1+cu128-cp311-cp311-manylinux_2_28_x86_64.whl
BSD-3-Clause
PyTorch (BSD-3-Clause) cu128 wheel, sha per download.pytorch.org index

LevinHarness/iggm-assets — internal asset cache

This private dataset is an internal staging cache of third-party runtime assets required by the Levin Harness plugin(s) listed below. It is not an official distribution: nothing here is published under this account's own terms, and it is not affiliated with or endorsed by any upstream project.

Ownership and licensing

  • Every file remains the property of its upstream authors.
  • Each file keeps its upstream license (see table and MANIFEST.json); terms are respected as-if downloaded from the original source, including any non-commercial restrictions.
  • Provided "as is", no warranty. For internal/offline reproducibility only.
  • Rights holders: this is a private cache; contact the account owner or delete the repo if you object to any content.

Licenses present: AFL-3.0, BSD-3-Clause, MIT, PD-academic.0, MIT, PD-academic.

Contents (mirrored verbatim, SHA-256 pinned)

path size upstream license
wheels/torch-2.7.1+cu128-cp311-cp311-manylinux_2_28_x86_64.whl 1.04 GB BSD-3-Clause
antibody_design_trunk.pth 101.7 MB MIT
antibody_fr_design_trunk.pth 101.6 MB MIT
antibody_inverse_design_trunk.pth 101.1 MB MIT
downloads/iggm-06abc563b3fc8c7ea020543add16b69b6f8a1c8d.tar.gz AFL-3.0
esm_ppi_650m_ab.pth 2.60 GB MIT
igso3_buffer.pth 371.4 MB MIT
stereo_chemical_props.txt 9 kB PD-academic

Provenance

MANIFEST.json records, per file: original URL(s) (with pinned revision), SHA-256, size, upstream license, which plugin(s) use it (used_by) and the plugin source file it was declared in (discovered_in).

Consumers must pin the repo commit in /resolve/<commit>/ URLs and re-verify the per-file SHA-256 before use.

  • Mirrored for: iggm
  • Files: 8 (4.32 GB known size)
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