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name: tuning-and-advanced description: "Guides TabPFN tuning, differentiable-input workflows, prompt tuning, and fine-tuning wrappers." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TabPFN tuning and advanced workflows

Use this sub-skill when the user is calibrating predictions, tuning thresholds, backpropagating through inputs, prompt-tuning, or fine-tuning the underlying TabPFN model.

Start here

• Read references/tuning-workflows.md for ordinary tuning config and metric selection.
• Read references/finetuning.md for FinetunedTabPFNClassifier and FinetunedTabPFNRegressor.
• Read references/differentiable-workflows.md for differentiable_input=True and prompt-tuning workflows.
• Read references/troubleshooting.md for early stopping, validation, and categorical-input failures.
• Run scripts/tuning_config_template.py --help to print a safe config template.

Use this sub-skill when

• The task mentions eval_metric, tuning_config, calibrate_temperature, or decision thresholds.
• The user wants prompt tuning, feature gradients, or other differentiable-input behavior.
• The task mentions FinetunedTabPFNClassifier or FinetunedTabPFNRegressor.
• The user wants to know how early stopping, validation frequency, or checkpoints behave during fine-tuning.

Route elsewhere

• Basic estimator usage and outputs: ../tabular-prediction/SKILL.md.
• Data cleaning, feature detection, or config fields: ../preprocessing-config/SKILL.md.
• Batched CV, fused multi-dataset scoring, or cache performance: ../batched-performance/SKILL.md.
• Model downloads, auth, cache, persistence, or checkpoint conversion: ../model-management/SKILL.md.

What this route owns

• Prediction calibration and decision-threshold tuning.
• Fine-tuning wrappers with early stopping and validation.
• Differentiable-input gradients and prompt-tuning loops.
• Advanced training / inference knobs that go beyond the base sklearn estimators.

What to remember

• Tuning is not the same as fine-tuning.
• Differentiable-input workflows require extra care with categorical columns.
• Fine-tuning wraps a TabPFN estimator but uses a different training loop.
• Validation and checkpointing matter because fine-tuning can be expensive.