tabpfn-mix-1.0-classifier by Autogluon: Open-Weight Model

Runs On

What it takes to serve tabpfn-mix-1.0-classifier (39M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	0.1 GB	0.1 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
8-bit	0.0 GB	0.0 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00
4-bit	0.0 GB	0.0 GB	1x MI300X (192 GB) Vultr	$1.85	1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

By Autogluon, published under apache-2.0, revision 80a938af912a.

TabPFNMix classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. TabPFNMix is based on a 12-layer encoder-decoder Transformer of 37 M parameters. We use a pre-training strategy incorporating in-context learning, similar to that used by TabPFN and TabForestPFN. To use TabPFNMix classifier, install AutoGluon by running: A minimal example showing how to perform fine-tuning and inference using the TabPFNMix classifier: If you find TabPFNMix useful for your research, please consider citing the associated papers: This project is licensed under the Apache-2.0 License.

Read Autogluon's full model card

TabPFNMix Classifier

TabPFNMix classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers.

Architecture

TabPFNMix is based on a 12-layer encoder-decoder Transformer of 37 M parameters. We use a pre-training strategy incorporating in-context learning, similar to that used by TabPFN and TabForestPFN.

Usage

To use TabPFNMix classifier, install AutoGluon by running:

pip install autogluon

A minimal example showing how to perform fine-tuning and inference using the TabPFNMix classifier:

import pandas as pd

from autogluon.tabular import TabularPredictor


if __name__ == '__main__':
    train_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
    subsample_size = 5000  
    if subsample_size is not None and subsample_size < len(train_data):
        train_data = train_data.sample(n=subsample_size, random_state=0)
    test_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')

    tabpfnmix_default = {
        "model_path_classifier": "autogluon/tabpfn-mix-1.0-classifier",
        "model_path_regressor": "autogluon/tabpfn-mix-1.0-regressor",
        "n_ensembles": 1,
        "max_epochs": 30,
    }

    hyperparameters = {
        "TABPFNMIX": [
            tabpfnmix_default,
        ],
    }

    label = "class"

    predictor = TabularPredictor(label=label)
    predictor = predictor.fit(
        train_data=train_data,
        hyperparameters=hyperparameters,
        verbosity=3,
    )

    predictor.leaderboard(test_data, display=True)

Citation

If you find TabPFNMix useful for your research, please consider citing the associated papers:

@article{erickson2020autogluon,
  title={Autogluon-tabular: Robust and accurate automl for structured data},
  author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},
  journal={arXiv preprint arXiv:2003.06505},
  year={2020}
}

@article{hollmann2022tabpfn,
  title={Tabpfn: A transformer that solves small tabular classification problems in a second},
  author={Hollmann, Noah and M{\"u}ller, Samuel and Eggensperger, Katharina and Hutter, Frank},
  journal={arXiv preprint arXiv:2207.01848},
  year={2022}
}

@article{breejen2024context,
  title={Why In-Context Learning Transformers are Tabular Data Classifiers},
  author={Breejen, Felix den and Bae, Sangmin and Cha, Stephen and Yun, Se-Young},
  journal={arXiv preprint arXiv:2405.13396},
  year={2024}
}

License

This project is licensed under the Apache-2.0 License.

Identity and Version

Repository: autogluon/tabpfn-mix-1.0-classifier
Publisher: Autogluon
Task: Tabular classification
Modality: Tabular
Library: Not stated by the source
Parameters: 39M parameters
Languages: Not stated by the source
Revision: 80a938af912a56967e8a62b8d36efba98adc116c
First published: 2024-11-22
Last updated: 2024-11-27

Files and Weights

4 files, 155.8 MB in total. The weights are 1 file totalling 155.8 MB in safetensors.

Weights1 file · 155.8 MB

Configuration1 file · 145 B

Documentation1 file · 2.7 KB

Repository1 file · 1.5 KB

Every file

File	Type	Size	SHA-256
model.safetensors	Weights	155.8 MB	8c271850e1f9
config.json	Configuration	145 B	—
README.md	Documentation	2.7 KB	—
.gitattributes	Repository	1.5 KB	—

License and Download

License: apache-2.0
Access: Open weights, no gate
Download size: 155.8 MB

Download from Autogluon

Released by Autogluon through its official repository on Hugging Face. Read the license.

Built From

Described by arXiv:2003.06505
Described by arXiv:2207.01848
Described by arXiv:2405.13396

Memory Requirements

Precision	Weights in memory
As published	155.8 MB
16-bit	0.1 GB
8-bit	0.0 GB
4-bit	0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About tabpfn-mix-1.0-classifier

How much GPU memory does tabpfn-mix-1.0-classifier need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (39M parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run tabpfn-mix-1.0-classifier on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use tabpfn-mix-1.0-classifier commercially?

Yes. tabpfn-mix-1.0-classifier is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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