Protenij — JAX/Equinox weights for Protenix
This repository hosts JAX/Equinox-converted model weights (and a mirror of the
original PyTorch protenix-v2 checkpoint) for use with
protenij, a JAX/Equinox translation
of Protenix, ByteDance's implementation
of the AlphaFold 3 architecture.
The JAX/Equinox weights are format conversions of the original PyTorch
checkpoints released by ByteDance — the underlying model parameters are
numerically identical, only the serialization format has changed (PyTorch .pt
→ Equinox .eqx + pickled skeleton).
Files
| File | Format | Size | Source |
|---|---|---|---|
protenix-v2.eqx / protenix-v2.skeleton.pkl |
Equinox | 1.86 GB | Converted from protenix-v2.pt |
protenix-v2.pt |
PyTorch | 1.86 GB | Mirror of upstream ByteDance release |
protenix_base_default_v1.0.0.eqx / .skeleton.pkl |
Equinox | — | Converted from upstream |
protenix_base_20250630_v1.0.0.eqx / .skeleton.pkl |
Equinox | — | Converted from upstream |
protenix_mini_default_v0.5.0.eqx / .skeleton.pkl |
Equinox | — | Converted from upstream |
protenix_tiny_default_v0.5.0.eqx / .skeleton.pkl |
Equinox | — | Converted from upstream |
components.v20240608.cif |
Data | — | CCD chemical components (upstream) |
components.v20240608.cif.rdkit_mol.pkl |
Data | — | CCD rdkit mol cache (upstream) |
clusters-by-entity-40.txt |
Data | — | PDB entity-40 clusters (upstream) |
Usage
from protenix.backend import load_model
model = load_model("protenix-v2") # auto-downloads from this repo
License and attribution
Released under the Apache License 2.0, matching the upstream bytedance/Protenix project.
The upstream Protenix README explicitly states:
"The Protenix project including both code and model parameters is released under the Apache 2.0 License. It is free for both academic research and commercial use."
Modification notice (Apache 2.0 §4(b))
The .eqx and .skeleton.pkl files in this repository are format
conversions of the original PyTorch checkpoints released by ByteDance. The
PyTorch state dicts were loaded and the tensors re-serialized in Equinox format
using
protenix/backend.py
and
translate_models.py.
No weights were retrained, fine-tuned, or otherwise numerically modified.
The protenix-v2.pt file in this repository is a bit-for-bit mirror of the
original PyTorch checkpoint hosted at
https://protenix.tos-cn-beijing.volces.com/checkpoint/protenix-v2.pt (mirrored
here after the upstream URL became unreachable).
Copyright notice (Apache 2.0 §4(c))
Copyright 2024 ByteDance and/or its affiliates. The original Protenix code and
model parameters were released under Apache License 2.0. See the LICENSE file
in this repository for the full license text.
Citations
If you use these weights, please cite the original Protenix work:
- Protenix repository: https://github.com/bytedance/Protenix
- Protenix technical reports in
docs/of the upstream repository
Disclaimer
These files are provided as-is. The weights are format conversions only — for the authoritative source and for training code, model cards, and technical reports, refer to the upstream ByteDance Protenix repository.