tabicl
TabICL: A state-of-the-art tabular foundation model
Decision gist · record as of 2026-08-14
Yes, if you work with tabular data and want a zero-tuning baseline or a scikit-learn-compatible alternative to gradient boosting. The permissive license, low install friction, active maintenance, and no known vulnerabilities make it safe to try. Caveat: torch is a heavy dependency, and GPU access is recommended for the speed claims; CPU-only inference may be slower than claimed.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- torch installation on Intel Macs may require conda install pytorch -c pytorch before pip install tabicl
- Low friction install with a pure-Python wheel.
- Requires torch, numpy, scipy, scikit-learn, and several supporting libraries; torch installation on Intel Macs may require conda first.
License · maintenance · safety
permissive license (permissive) — Dual-licensed under BSD 3-Clause (primary) and Apache 2.0 (for forecast module code derived from TabPFN-TS). Both are permissive; you may use, modify, and distribute freely with attribution and no warranty.
last release 2026-04-29 (107 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 142,950 downloads/mo, #11,193 on PyPI
Alternatives
Verify before relying
pip install tabicl
from tabicl import TabICLClassifier
clf = TabICLClassifier()
clf.fit(X_train, y_train)
clf.predict(X_test)- Whether GPU acceleration is required for the claimed performance on 50,000 samples, or if CPU inference is practical
- Exact memory overhead of KV caching feature for typical dataset sizes
- Whether pre-trained checkpoint auto-download works reliably behind corporate proxies
What it is and what it does
TabICL is a pre-trained transformer model designed to handle tabular classification and regression tasks without hyperparameter tuning. It works by performing in-context learning—learning from training data within a single forward pass—and can be used like a scikit-learn estimator with fit() and predict() methods. The model comes with pre-trained checkpoints and downloads them automatically on first use.
The package supports datasets ranging from hundreds to hundreds of thousands of samples with up to 2,000 features. It includes optional fine-tuning for single-dataset adaptation, KV caching to speed up repeated inference on the same training data, and CPU/disk offloading for larger datasets. Core dependencies are torch, numpy, scipy, scikit-learn, einops, huggingface-hub, psutil, and tqdm.
Use it for
- Quick baseline predictions on tabular datasets without spending time tuning hyperparameters
- Classification or regression on datasets with hundreds to tens of thousands of samples where GPU inference is available
- Fine-tuning a pre-trained model on a single important dataset using FinetunedTabICLClassifier or FinetunedTabICLRegressor
- Repeated inference on the same training data with KV caching enabled to reduce latency
- Handling datasets with many features (up to 2,000) where traditional tabular models may struggle
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with tabular data and want a zero-tuning baseline or a scikit-learn-compatible alternative to gradient boosting.
The permissive license, low install friction, active maintenance, and no known vulnerabilities make it safe to try. Caveat: torch is a heavy dependency, and GPU access is recommended for the speed claims; CPU-only inference may be slower than claimed.
Install
tabicl on PyPI
Before you install
Low friction install with a pure-Python wheel. Requires torch, numpy, scipy, scikit-learn, and several supporting libraries; torch installation on Intel Macs may require conda first. Actively maintained with recent releases.
torch installation on Intel Macs may require conda install pytorch -c pytorch before pip install tabicl
License in practice
Dual-licensed under BSD 3-Clause (primary) and Apache 2.0 (for forecast module code derived from TabPFN-TS). Both are permissive; you may use, modify, and distribute freely with attribution and no warranty.
Quickstart
pip install tabicl
from tabicl import TabICLClassifier
clf = TabICLClassifier()
clf.fit(X_train, y_train)
clf.predict(X_test)Verify before relying
- Whether GPU acceleration is required for the claimed performance on 50,000 samples, or if CPU inference is practical
- Exact memory overhead of KV caching feature for typical dataset sizes
- Whether pre-trained checkpoint auto-download works reliably behind corporate proxies
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packageseinopshuggingface-hubnumpypsutilscikit-learnscipytorchtqdm |
| Maintenance | Actively maintained 107 days since the last release |
| First released | |
| Downloads | 142,950 / month, #11,193 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: tabicl-2.1.1-py3-none-any.whl
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See also tabpfn · pytabkit · tabpfn-common-utils · autogluon.tabular · scikit-multilearn · skforecast · libcuml-cu12 · autogluon.features · ngboost · scikit-plot