Add dataset card and link to paper

This PR improves the dataset card for LimiX-2M. It adds YAML metadata, links to the research paper, the official GitHub repository, and the project page. It also includes a sample usage snippet for classification tasks as found in the GitHub documentation.

README.md

@@ -1,3 +1,66 @@
----
-license: apache-2.0
----
+---
+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}
+}
+```