---
license: apache-2.0
task_categories:
- other
tags:
- tabular
- foundation-model
---

This repository contains the artifacts for **LimiX-2M**, a 2M-parameter tabular foundation model designed to mitigate low-rank collapse and attention bottlenecks in structured data.

- **Paper:** [LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models](https://huggingface.co/papers/2606.04485)
- **GitHub Repository:** [https://github.com/limix-ldm-ai/LimiX](https://github.com/limix-ldm-ai/LimiX)
- **Project Page:** [https://www.limix.ai/](https://www.limix.ai/)

## Model Description
LimiX-2M utilizes a unified *tokenize-and-route* framework. It expands scalar features into compact localized RBF features (RaBEL) and uses a reordered bidirectional block (S$\rightarrow$N$\rightarrow$F) to align computation with the readout. This architecture allows the model to outperform larger baselines while reducing training and inference costs.

## Sample Usage

The following example demonstrates how to use the `LimiXPredictor` for a classification task. Note that using the predictor requires the source code from the [GitHub repository](https://github.com/limix-ldm-ai/LimiX).

```python
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split
from huggingface_hub import hf_hub_download
import torch
import numpy as np
import os
from inference.predictor import LimiXPredictor

# Setup environment
os.environ["RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "29500"

# Load data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)

# Download model
model_file = hf_hub_download(repo_id="stableai-org/LimiX-2M", filename="LimiX-2M.ckpt", local_dir="./cache")

# Initialize and predict
clf = LimiXPredictor(
    device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'), 
    model_path=model_file, 
    inference_config='config/cls_default_retrieval.json'
)
prediction = clf.predict(X_train, y_train, X_test)

print("roc_auc_score:", roc_auc_score(y_test, prediction[:, 1]))
print("accuracy_score:", accuracy_score(y_test, np.argmax(prediction, axis=1)))
```

## Citation
```bibtex
@article{limix2m2026,
  title={LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models},
  author={Zhang, Xingxuan and others},
  journal={arXiv preprint arXiv:2606.04485},
  year={2026}
}
```