Add dataset card for LimiX-2M

README.md

@@ -1,3 +1,63 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- other
+tags:
+- tabular
+- foundation-model
+---
+
+# LimiX-2M
+
+LimiX-2M is a 2M-parameter tabular foundation model (TFM) introduced in the paper [LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models](https://huggingface.co/papers/2606.04485). It utilizes a unified tokenize-and-route framework to improve conditioning and shallow-layer effective rank, outperforming larger baselines on widely used tabular benchmarks.
+
+- **Project Page:** [https://www.limix.ai/](https://www.limix.ai/)
+- **GitHub Repository:** [https://github.com/limix-ldm-ai/LimiX](https://github.com/limix-ldm-ai/LimiX)
+
+## Sample Usage
+
+The following example demonstrates how to use the `LimiXPredictor` for a classification task as described in the repository's documentation:
+
+```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 numpy as np
+import torch
+import os, sys
+
+# Set environment variables for initialization
+os.environ["RANK"] = "0"
+os.environ["WORLD_SIZE"] = "1"
+os.environ["MASTER_ADDR"] = "127.0.0.1"
+os.environ["MASTER_PORT"] = "29500"
+
+# Assuming the LimiX repository is cloned and in the path
+from inference.predictor import LimiXPredictor
+
+# 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 checkpoint
+model_file = hf_hub_download(repo_id="stableai-org/LimiX-2M", filename="LimiX-2M.ckpt", local_dir="./cache")
+
+# Initialize predictor 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{zhang2025limix,
+  title={Limix: Unleashing structured-data modeling capability for generalist intelligence},
+  author={Zhang, Xingxuan and Ren, Gang and Yu, Han and Yuan, Hao and Wang, Hui and Li, Jiansheng and Wu, Jiayun and Mo, Lang and Mao, Li and Hao, Mingchao and others},
+  journal={arXiv preprint arXiv:2509.03505},
+  year={2025}
+}
+```