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name: pretrained-and-evaluation description: "Guide Graphormer pretrained checkpoint loading, fine-tuning, and evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Pretrained And Evaluation

Use this sub-skill when the task is to choose a Graphormer pretrained model, render a safe fine-tuning command, evaluate an official pretrained checkpoint, or evaluate checkpoints saved by a Graphormer run.

Do not use this sub-skill for base training templates, custom dataset implementation, or model architecture internals. Route those questions to the Graphormer training, dataset/customization, or model-extension sub-skills instead.

Bundled Runtime Materials

• Read references/pretrained-and-evaluation.md for pretrained model names, output-layer loading semantics, MolHIV FLAG fine-tuning knobs, and evaluation/checkpoint behavior.
• Read references/troubleshooting.md for checkpoint download/cache issues, output-head mismatches, CUDA-only evaluation behavior, metric mistakes, OGB split/evaluator mismatches, and strict checkpoint loading failures.
• Use scripts/build_graphormer_eval_or_finetune_command.py to render, but never execute, safe Graphormer evaluation or MolHIV fine-tuning commands.

Operating Boundary

The bundled helper prints shell commands only. Treat any rendered command as a reviewable plan: check dataset availability, checkpoint source, GPU availability, output-layer decision, and metric before running it in a separate Researcher execution session.