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sokrypton
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af3-any-model

alphafold3
protein-structure-prediction
biology
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squash history: keep only the current weights (drops superseded blob versions)

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  1. .gitattributes +35 -0
  2. README.md +83 -0
  3. boltz2/boltz2.bin.zst +3 -0
  4. boltz2/boltz2.int8.bin.zst +3 -0
  5. chai1/chai1.bin.zst +3 -0
  6. chai1/chai1.int8.bin.zst +3 -0
  7. esmfold2/esmfold2.bin.zst +3 -0
  8. esmfold2/esmfold2.int8.bin.zst +3 -0
  9. esmfold2/esmfold2.lm.npz +3 -0
  10. esmfold2/esmfold2_exp.bin.zst +3 -0
  11. esmfold2/esmfold2_exp.int8.bin.zst +3 -0
  12. esmfold2/esmfold2_exp.lm.npz +3 -0
  13. esmfold2/esmfold2_exp_cutoff2025.bin.zst +3 -0
  14. esmfold2/esmfold2_exp_cutoff2025.int8.bin.zst +3 -0
  15. esmfold2/esmfold2_exp_cutoff2025.lm.npz +3 -0
  16. esmfold2/esmfold2_exp_fast.bin.zst +3 -0
  17. esmfold2/esmfold2_exp_fast.int8.bin.zst +3 -0
  18. esmfold2/esmfold2_exp_fast.lm.npz +3 -0
  19. esmfold2/esmfold2_exp_fast_cutoff2025.bin.zst +3 -0
  20. esmfold2/esmfold2_exp_fast_cutoff2025.int8.bin.zst +3 -0
  21. esmfold2/esmfold2_exp_fast_cutoff2025.lm.npz +3 -0
  22. esmfold2/esmfold2_fast.bin.zst +3 -0
  23. esmfold2/esmfold2_fast.int8.bin.zst +3 -0
  24. esmfold2/esmfold2_fast.lm.npz +3 -0
  25. esmfold2/esmfold2_lm300m.bin.zst +3 -0
  26. esmfold2/esmfold2_lm300m.int8.bin.zst +3 -0
  27. esmfold2/esmfold2_lm300m.lm.npz +3 -0
  28. esmfold2/esmfold2_lm600m.bin.zst +3 -0
  29. esmfold2/esmfold2_lm600m.int8.bin.zst +3 -0
  30. esmfold2/esmfold2_lm600m.lm.npz +3 -0
  31. intellifold2/intellifold2.bin.zst +3 -0
  32. intellifold2/intellifold2.int8.bin.zst +3 -0
  33. lm/esm2.bin.zst +3 -0
  34. lm/esmc.bin.zst +3 -0
  35. lm/esmc_300m.bin.zst +3 -0
  36. lm/esmc_600m.bin.zst +3 -0
  37. opendde/opendde.bin.zst +3 -0
  38. opendde/opendde.int8.bin.zst +3 -0
  39. openfold3/openbind0.bin.zst +3 -0
  40. openfold3/openbind0.int8.bin.zst +3 -0
  41. openfold3/openfold3.bin.zst +3 -0
  42. openfold3/openfold3.int8.bin.zst +3 -0
  43. protenix/protenix05.bin.zst +3 -0
  44. protenix/protenix05.int8.bin.zst +3 -0
  45. protenix/protenix1.bin.zst +3 -0
  46. protenix/protenix1.int8.bin.zst +3 -0
  47. protenix/protenix1_20250630.bin.zst +3 -0
  48. protenix/protenix1_20250630.int8.bin.zst +3 -0
  49. protenix/protenix2.bin.zst +3 -0
  50. protenix/protenix2.int8.bin.zst +3 -0
.gitattributes ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ckpt filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.npz filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.ot filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ license: other
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+ license_name: see-per-model-licences
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+ tags:
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+ - protein-structure-prediction
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+ - alphafold3
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+ - biology
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+ library_name: alphafold3
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+ ---
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+
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+ # AlphaFold3-family model weights, converted to run in one package
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+
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+ Seven published AlphaFold3-architecture models, converted from their original
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+ PyTorch checkpoints into the AlphaFold 3 JAX/Haiku parameter format so that a
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+ single codebase runs all of them from the same input JSON.
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+
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+ Code: [sokrypton/alphafold3, branch `af3-any-model`](https://github.com/sokrypton/alphafold3/tree/af3-any-model)
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+
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+ ```bash
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+ # the weights are fetched on first use; nothing to download by hand
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+ python run_alphafold.py --model=boltz2 --json_path=fold_input.json \
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+ --output_dir=out --norun_data_pipeline
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+ ```
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+
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+ Each model ships two files: `<model>.bin.zst`, the parameters, and
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+ `<model>.shapes.json`, the parameter tree derived from the graph plus a record
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+ of which converter produced the blob and when — it is what
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+ makes loading skip `jax.eval_shape`, and it names anything a conversion did not
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+ cover. **All seven conversions cover every parameter the graph asks for.**
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+
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+ ## What is here, and whose it is
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+
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+ These are DERIVED works: the same trained parameters, rewritten into another
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+ framework's layout. Each remains under its original licence and belongs to its
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+ original authors.
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+
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+ | file | model | authors | licence | original |
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+ |---|---|---|---|---|
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+ | `openfold3.*` | OpenFold3 | AlQuraishi Lab / OpenFold Consortium | Apache-2.0 | [openfold3](https://github.com/aqlaboratory/openfold3) |
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+ | `intellifold2.*` | IntelliFold-v2 | IntelligenAI | Apache-2.0 | [intelligenAI/intellifold](https://huggingface.co/intelligenAI/intellifold) |
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+ | `opendde.*` | OpenDDE | Aureka Research | Apache-2.0 | [aurekaresearch/OpenDDE](https://huggingface.co/aurekaresearch/OpenDDE) |
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+ | `boltz2.*` | Boltz-2 | Wohlwend et al., MIT | MIT | [jwohlwend/boltz](https://github.com/jwohlwend/boltz) |
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+ | `protenix2.*` | Protenix-v2 | ByteDance | Apache-2.0 | [bytedance/Protenix](https://github.com/bytedance/Protenix) |
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+ | `rosettafold3.*` | RoseTTAFold3 | Institute for Protein Design, UW | BSD-3-Clause | [RosettaCommons foundry](https://files.ipd.uw.edu/pub/rf3/) |
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+ | `chai1.*` | chai-1 | Chai Discovery | Apache-2.0 | [chaidiscovery/chai-lab](https://github.com/chaidiscovery/chai-lab) |
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+
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+ If you use one of these, cite the model's own authors.
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+
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+ **AlphaFold 3's own parameters are not here and will not be.** Google DeepMind
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+ requires you to request them directly; point `--model_dir` at your own copy.
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+
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+ ## Notes on two of them
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+
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+ * **chai-1** also needs `std_conformers.npz` (in this repo, fetched with it) and
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+ ESM2 token embeddings, which are most of its token feature stream. Without the
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+ embeddings it is a different model — see `converters/esm_embed.py` and
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+ `--esm_embeddings`.
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+ * **protenix2** was converted from a community mirror of ByteDance's release,
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+ since the official CDN was unreachable; the SHA256 was verified against the
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+ CDN copy while it still resolved.
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+
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+ ## How they were made
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+
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+ `python -m converters.convert --model NAME --out DIR` in the repo above, which
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+ downloads the published checkpoint, converts it, and writes the shape manifest.
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+ The conversion is not mechanical — residue alphabets differ between codebases,
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+ as do the row/column conventions of pair projections, and getting either wrong
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+ is silent. `OF3_AF3_PORTING_NOTES.md` and `docs/ported_models.md` record what
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+ each one required.
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+
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+ ## Sanity check
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+
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+ All seven fold 6MRR (a de novo designed 68-residue protein) from a single
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+ sequence, scored against the crystal structure, at 3 recycles and 1 sample:
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+
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+ | model | CA-RMSD | pLDDT | | model | CA-RMSD | pLDDT |
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+ |---|---|---|---|---|---|---|
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+ | boltz2 | 0.52 Å | 96.8 | | opendde | 1.59 Å | 92.0 |
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+ | protenix2 | 0.67 Å | 84.8 | | intellifold2 | 1.63 Å | 85.5 |
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+ | rosettafold3 | 0.99 Å | 81.5 | | openfold3 | 1.74 Å | 78.6 |
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+ | | | | | chai1 | 1.75 Å | 84.5 |
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+
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+ (AlphaFold 3 itself gets 0.61 Å on the same input, for reference.)
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