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name: model-inference description: "Construct bounded AlphaFold 3 models, choose forward loss or sampling modes, load compatible checkpoints, and inspect confidence, distogram, and ranking outputs." disable-model-invocation: true metadata: disco-role: operating license: MIT

Model inference

Use this sub-skill after an input has been converted to model-ready tensors, or use the tiny smoke helper to validate the model contract before allocating a large run. Start with workflows, then consult the API contract for exact shapes and troubleshooting for recovery.

Route by responsibility

• Send proteins, nucleic acids, ligands, metals, atom features, batching, masks, representative-atom indices, and output structure conversion to input-representation.
• Send mmCIF/PDB parsing, MSA and template acquisition or preprocessing to data-pipeline. This model accepts already prepared tensors; it does not fetch biological data for you.
• Send Trainer, datasets, optimizers, YAML, EMA, and checkpointed training loops to training-configuration.
• Send Click command construction, output files, and Gradio operation to cli-serving.

Operating sequence

1. Select CPU for contract checks and reduced tests; select CUDA only after the device, dtype, and memory budget are explicit.
2. Construct a deliberately reduced Alphafold3 with dimensions and depths appropriate to the available memory. The constructor's production defaults are not a smoke configuration.
3. Validate atom_inputs, atom-pair inputs, token counts, molecule lengths, masks, and all index offsets before calling forward.
4. Choose the return mode deliberately: coordinates for inference, or loss and an optional breakdown when ground-truth positions/labels are supplied.
5. Add confidence/distogram logits only when they are needed; rank multiple samples/models with the dedicated scoring classes rather than treating raw logits as scores.
6. For a checkpoint, prefer init_and_load when the file was saved by this package, and verify version, constructor dimensions, device, and strictness before production inference.

Run the bundled safe check. The examples assume this sub-skill directory is the current directory; from any other directory, invoke the linked script by its resolved path because the helper has no current-directory assumptions:

python scripts/smoke_model.py --help
python scripts/smoke_model.py --mode signature --device cpu
# Only for an explicitly bounded tiny forward:
python scripts/smoke_model.py --mode forward --device cpu --num-sample-steps 2
python scripts/smoke_model.py --mode forward --device cpu --num-sample-steps 2 --with-distogram

The helper never downloads weights, enables PLM/NLM encoders, trains, starts a server, or uses production-scale defaults. It is a contract probe, not evidence of useful structural accuracy or production throughput.