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README.md

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-license: mit
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+---
+license: mit
+---
+
+<div align="center">
+  <a href="https://lexsi.ai/">
+    <img src="https://img.shields.io/badge/Lexsi-Homepage-FF6B6B?style=for-the-badge" alt="Homepage"/>
+  </a>
+  <a href="https://huggingface.co/Lexsi">
+    <img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Lexsi AI-FFD21E?style=for-the-badge" alt="Hugging Face"/>
+  </a>
+  <a href="https://discord.gg/dSB62Q7A">
+    <img src="https://img.shields.io/badge/Discord-Join-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Discord"/>
+  </a>
+  <a href="https://github.com/Lexsi-Labs/Orion-BiX">
+    <img src="https://img.shields.io/badge/GitHub-Repository-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"/>
+  </a>
+</div>
+
+
+
+# Orion-BiX: Bi-Axial Meta-Learning Model for Tabular In-Context Learning
+
+**Orion-BiX** is an advanced tabular foundation model that combines **Bi-Axial Attention** with **Meta-Learning** capabilities for few-shot tabular classification. The model extends the TabICL architecture with alternating attention patterns and episode-based training.
+
+The model is part of **Orion**, a family of tabular foundation models with various enhancements.
+
+### Key Innovations
+
+1. **Bi-Axial Attention**: Alternating attention patterns (Standard → Grouped → Hierarchical → Relational) that capture multi-scale feature interactions within tabular data
+2. **Meta-Learning with k-NN Support Selection**: Episode-based training with intelligent support set selection using similarity metrics
+3. **Three-Component Architecture**: Column embedding (Set Transformer), Bi-Axial row interaction, and In-Context Learning prediction
+
+### Architecture Overview
+
+```
+Input → tf_col (Set Transformer) → Bi-Axial Attention → tf_icl (ICL) → Output
+```
+**Component Details:**
+- **tf_col (Column Embedder)**: Set Transformer for statistical distribution learning across features
+- **Bi-Axial Attention**: Replaces standard RowInteraction with alternating attention patterns:
+  - Standard Cross-Feature Attention
+  - Grouped Feature Attention
+  - Hierarchical Feature Attention
+  - Relational Feature Attention
+  - CLS Token Aggregation
+- **tf_icl (ICL Predictor)**: In-context learning module for few-shot prediction
+
+
+## Usage
+
+```python
+from orion_bix.sklearn import OrionBiXClassifier
+
+# Initialize and use
+clf = OrionBiXClassifier()
+clf.fit(X_train, y_train)
+predictions = clf.predict(X_test)
+```
+
+This code will automatically download the pre-trained model from Hugging Face and use a GPU if available.
+
+## Installation
+
+### From the source
+#### Option 1: From the local clone
+
+```bash
+cd orion-bix
+pip install -e .
+```
+
+#### Option 2: From the Git Remote
+
+```bash
+pip install git+https://github.com/Lexsi-Labs/Orion-BiX.git
+```
+
+## Citation
+
+If you use Orion-BiX in your research, please cite:
+
+```bibtex
+@misc{bouadi25oriobix,
+  title={Orion-BiX: Bi-Axial Meta-Learning for Tabular In-Context Learning},
+  author={Mohamed Bouadi and Pratinav Seth and Aditya Tanna and Vinay Kumar Sankarapu},
+  year={2025},
+}
+```