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name: preprocessing-config description: "Guides TabPFN input validation, feature-modality detection, DataFrame cleanup, and inference/preprocessing configuration." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TabPFN preprocessing and configuration

Use this sub-skill when the user is asking about what shapes, dtypes, feature types, missing values, or configuration fields TabPFN accepts before fitting or predicting.

Start here

• Read references/data-validation.md for shape checks, DataFrame cleanup, text and categorical handling, and infinity behavior.
• Read references/inference-config.md for InferenceConfig, PreprocessorConfig, and the settings fields that control preprocessing.
• Read references/troubleshooting.md for the most common validation failures and recovery steps.
• Run scripts/inspect_inference_config.py --help to inspect the installed defaults without downloading model weights.

Use this sub-skill when

• The task is about DataFrame columns, nullable dtypes, categorical indices, text-like columns, or missing values.
• The user wants to know whether NaN, inf, or string columns are allowed.
• The user asks about InferenceConfig, PreprocessorConfig, or env vars such as TABPFN_ALLOW_CPU_LARGE_DATASET or TABPFN_MPS_MEMORY_FRACTION.
• The user needs to understand which feature columns are treated as categorical or numerical.
• The user wants to know why a validation error is raised before fit/predict begins.

Route elsewhere

• Ordinary estimator usage, logits, quantiles, or embeddings: ../tabular-prediction/SKILL.md.
• Multi-dataset batched scoring or cache/performance tuning: ../batched-performance/SKILL.md.
• Tuning, differentiable input, and fine-tuning: ../tuning-and-advanced/SKILL.md.
• Model downloads, auth, cache, persistence, or checkpoint conversion: ../model-management/SKILL.md.

What this route owns

• Input validation before fit and predict.
• Detection of numerical, categorical, text, and constant features.
• Cleanup of mixed pandas / NumPy inputs.
• Explicit configuration of preprocessing and inference defaults.
• CPU-size and MPS memory guardrails.

What to remember

• TabPFN can handle mixed tabular data, but not every string column is a good feature.
• Free text is usually ordinal-encoded as a noisy high-cardinality categorical feature.
• PASSTHROUGH_INF=True changes whether infinities are rejected or temporarily preserved.
• ignore_pretraining_limits=True overrides model size guardrails, but it does not make a huge CPU dataset fast.