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name: advanced-model-configuration description: "Inspect and troubleshoot Protenix configs, model internals, kernel backends, TFG guidance, confidence utilities, metrics, and safe runtime fallbacks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Protenix Advanced Model Configuration

Use this sub-skill for expert Protenix internals work: configuration parsing, model-type defaults, optional CUDA/Triton/cuEquivariance/DeepSpeed paths, conservative backend fallbacks, Training-Free Guidance (TFG), confidence post-processing, and direct metrics calls.

Start Here

1. Run python scripts/protenix_runtime_doctor.py --json for a read-only report of package metadata, PyTorch/CUDA, optional backend imports, relevant environment switches, CLI availability, and external tool availability.
2. Read references/config-api.md before using parse_configs, saving configs, composing dotted overrides, or comparing model-name-specific defaults.
3. Read references/kernels-and-backends.md before changing LAYERNORM_TYPE, triangle_attention, triangle_multiplicative, --triatt_kernel, --trimul_kernel, TF32, cache, fusion, dtype, cuEquivariance, Triton, or DeepSpeed behavior.
4. Read references/tfg-confidence-and-metrics.md before enabling TFG, editing potential terms, calling confidence helpers, interpreting pTM/gPDE/PAE outputs, or using RMSD/LDDT metrics directly.
5. Read references/troubleshooting.md when optional dependency imports, CUDA visibility, ABI mismatches, kernel-image failures, config overrides, TFG feature validation, or metric tensor shapes fail.

Bundled Tool

• scripts/protenix_runtime_doctor.py: read-only runtime checker with text and --json output. It does not run kernels, compile extensions intentionally, download checkpoints, run inference, run training, or inspect local source paths.
• Add --include-model-imports only when import probing is the goal; model-module imports may trigger fast layer norm behavior unless LAYERNORM_TYPE=torch is already set.

Route Elsewhere

• Routine prediction command assembly, checkpoint/cache download behavior, output folders, and CLI inference workflows belong to ../cli-and-inference/SKILL.md.
• Protenix input JSON authoring, ligands, bonds, constraints, seed fields, and feature-file paths belong to ../input-data-and-features/SKILL.md.
• Training data preparation, database layout, fine-tuning launches, optimizer/scheduler usage, and distributed training belong to ../training-and-data-pipeline/SKILL.md.

Safety Rules

• Prefer reversible runtime/config overrides before code changes: triangle_attention="torch", triangle_multiplicative="torch", --triatt_kernel torch, --trimul_kernel torch, and LAYERNORM_TYPE=torch are the first isolation path.
• Set LAYERNORM_TYPE=torch before importing model modules when the task is config inspection, CPU debugging, tensor-shape debugging, or fallback triage rather than proving the fused layer norm extension works.
• Treat cuEquivariance, Triton, and DeepSpeed as optional acceleration paths. Do not promise they work on every GPU or ABI combination; verify imports, CUDA visibility, and selected config values first.
• Treat TFG, confidence, and metrics symbols as current implementation entry points for expert debugging, not as a stable public API contract.