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name: embeddings description: "Guides ESM, MSA Transformer, ProtTrans/ProtBERT, and precomputed sequence/MSA representations for Alphafold2, including projection, masks, caches, downloads, Apex, and OOM diagnosis." disable-model-invocation: true metadata: disco-role: operating license: MIT

Embeddings

Use this sub-skill for ESMEmbedWrapper, MSAEmbedWrapper, ProtTranEmbedWrapper, external sequence/MSA features, precomputed representations, projection-width checks, or failures involving model assets, torch.hub, Hugging Face, transformers, Apex, fused operations, caches, and memory pressure.

Route the task

• For exact wrapper signatures, tensor contracts, masks, projection rules, and disable_token_embed, read API reference.
• Before constructing any pretrained wrapper, read External models. Construction can resolve code and large weights; never trigger that acquisition without approval.
• For missing assets, network/cache errors, source-shape defects, Apex/fused failures, width mismatch, or OOM, use Troubleshooting.
• For a safe CPU-default check, run embedding_input_smoke.py. It creates a synthetic (B, M, N, 1280) tensor, checks the core projection, and consumes projected representations without constructing or downloading a pretrained model.

Operating sequence

1. Decide between a live pretrained wrapper and already available representations. Prefer precomputed inputs when model source, weights, cache, network policy, fused kernels, or memory have not been approved and validated.
2. Validate seq: (B, N), msa: (B, M, N), boolean masks on the same residue axes, and the external feature width before allocating a model.
3. Match the external width to the correct projection: wrapper projections end at Alphafold2.dim; the core embedd_project accepts num_embedds channels (1280 by default) and also ends at Alphafold2.dim.
4. Review the 0.4.32 source limitations before relying on a wrapper or on the core argument spelled embedds. The README describes the intended workflow, but several wrapper/helper paths and the direct embedds branch drift from that description.
5. Run only a bounded, explicit device check. The pretrained wrapper paths are not offline merely because alphafold2_pytorch imports successfully.

Boundaries

• General Alphafold2 trunk construction, attention, and output contracts: core-model.
• Coordinates, confidence, refinement, and recycling: structure-and-recycling.
• Metrics and unrelated utilities: utilities.

A wrapper computes seq_embed and msa_embed and forwards them to the trunk. The lower-level embedds argument is a different interface; in 0.4.32 its branch is shadowed by MSA initialization. Do not describe a successful wrapper or functional direct-embedds inference unless that exact path was validated.