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name: model-api description: "Use OmegaFold programmatically: configs, model construction, forward inputs and outputs, confidence helpers, architecture choices, and API debugging." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

OmegaFold Model API

Use this sub-skill when an agent needs to call OmegaFold from Python, inspect its model/config API, load weights manually, or diagnose backend and dependency failures. For end-user command recipes, use inference CLI. For FASTA normalization, generated input tensors, and PDB output details, use data and outputs.

Quick Routes

• Inspect installed API safely with scripts/inspect_model_api.py; it imports OmegaFold, prints signatures/config summaries, and avoids downloads.
• Build configs and call the model with references/api-reference.md.
• Understand model 1 vs model 2 and major modules with references/model-architecture.md.
• Debug invalid model ids, state-dict mismatches, NumPy/Torch/CUDA/MPS issues, and API OOM with references/troubleshooting.md.

Programmatic Inference Shape

A safe custom API flow is: create cfg = omegafold.make_config(1 or 2), instantiate omegafold.OmegaFold(cfg), load a matching state dict, move to a real device, prepare inputs through omegafold.pipeline.fasta2inputs, call model(input_data, predict_with_confidence=True, fwd_cfg=forward_config), then write PDBs through the data/output sub-skill.

Do not present OmegaFold as a training or fine-tuning toolkit. This release is inference code; unsupported training loops, gradient recipes, optimizer setup, or model surgery are outside this sub-skill.