To fit a pretrained TabSTAR model to your own dataset, install the package: Paper: TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations
Open-weight model · Tabular classification
tabpfn-mix-1.0-classifier
by Autogluon autogluon/tabpfn-mix-1.0-classifier
tabpfn-mix-1.0-classifier is an open-weight model for tabular classification from Autogluon, released under Apache License 2.0. It has 39M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 906 downloads a month.
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.
Parameters39M
Context—
Weights155.8 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads906
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 Oct 16, 2026.
tabpfn-mix-1.0-classifier on every accelerator the SAVRN Index prices, at every precision
Model Card
By Autogluon, published under apache-2.0, revision 80a938af912a.
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
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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