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core-model

core-model

Guide construction and execution of the verified Alphafold2 sequence/MSA trunk and direct Evoformer API, including distogram and angle-logit prediction, masks, templates, extra MSA inputs, embeddings, and supported trunk configuration.

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Skill-Dokumentation

GitHub

name: core-model description: "Guide construction and execution of the verified Alphafold2 sequence/MSA trunk and direct Evoformer API, including distogram and angle-logit prediction, masks, templates, extra MSA inputs, embeddings, and supported trunk configuration." disable-model-invocation: true metadata: disco-role: operating license: MIT

Core model

Use this skill when the request is to build or run alphafold2_pytorch.Alphafold2, configure its Evoformer trunk, supply sequence/MSA/template features or masks, or diagnose core shape and output-routing errors.

Route first

  • Read the API reference before choosing constructor or forward arguments.
  • Use the workflows for small, CPU-safe synthetic calls and output inspection. The bundled smoke helper is deterministic, has no network or data-file step, and defaults to CPU.
  • Route coordinate refinement, confidence, and recycling semantics to structure-and-recycling.
  • Route pretrained ESM/MSA/ProtTrans wrappers and downloaded embedding models to embeddings.
  • Route distance metrics, bucket utilities, and MDS to utilities.
  • For import, dimension, memory, or README/source-drift failures, use troubleshooting.

Minimum safe recipe

  1. Install distribution alphafold2-pytorch==0.4.32 with its native scientific dependencies, then verify import alphafold2_pytorch.
  2. Construct Alphafold2(dim=16, depth=1, heads=1, dim_head=16) and call model.eval() with torch.no_grad() for inference.
  3. Pass seq as integer shape (B, N), an optional integer msa shape (B, M, N), and boolean mask/msa_mask with matching shapes. The inspected source asserts that MSA width equals primary-sequence width; do not follow the README examples that use different widths.
  4. Read ReturnValues.distance for distogram logits. Set predict_angles=True to additionally read theta_logits, phi_logits, and omega_logits; the source returns an object, not the tuple shown in the README.
  5. For already embedded pair/MSA representations, use the direct public Evoformer route in the API reference; it returns updated representations and does not produce distogram or angle logits itself.

The exact contracts, source/installed provenance, template and embedding dimensions, supported knobs, and drift warnings are in the linked references. Do not claim README-only flags such as reversible, use_conv, sparse/linear/Kronecker/compressed attention, or custom_block_types are constructor features at this version: they are absent from the inspected public signature.

Gespeicherte Skills