Update README.md

README.md

@@ -16,7 +16,11 @@ license: mit
     <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-MSP">
-    <img src="https://img.shields.io/badge/GitHub-Repository-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"/>
+    <img src="https://img.shields.io/badge/GitHub-Orion%20MSP-181717?style=for-the-badge&logo=github&logoColor=white" alt="Orion MSP GitHub"/>
+  </a>
+  <!-- TabTune repo -->
+  <a href="https://github.com/Lexsi-Labs/TabTune">
+    <img src="https://img.shields.io/badge/GitHub-TabTune-181717?style=for-the-badge&logo=github&logoColor=white" alt="TabTune GitHub"/>
   </a>
 </div>
 
@@ -25,6 +29,8 @@ license: mit
 
 Orion-MSP is a tabular foundation model for in-context learning. It uses multi-scale sparse attention and Perceiver-style memory to process tabular data at multiple granularities, capturing both local feature interactions and global dataset-level patterns.
 
+OrionMSP can be used either directly via its own Python package or through [TabTune](https://github.com/Lexsi-Labs/TabTune), which provides a unified interface over several tabular foundation models.
+
 ## Key Features
 
 - **Multi-Scale Sparse Attention:** Processes features at three levels (scales 1, 4, 16) using windowed, global, and random attention patterns, reducing quadratic complexity to near-linear.
@@ -161,7 +167,7 @@ Orion-MSP consists of four main components:
 </div>
 
 Orion-MSP is the most consistent top performer across all three benchmarks, achieving the best overall rank.
-- On TALENT, it ranks ***1** overall, while TabPFN edges the highest ACC/F1 by a hair.
+- On TALENT, it ranks **1** overall, while TabPFN edges the highest ACC/F1 by a hair.
 - On OpenML-CC18, Orion-MSP attains the top ACC/F1 (0.8722/0.8676), narrowly ahead of TabPFN and TabDPT.
 - On TabZilla, it leads with the highest ACC/F1 and the best rank.
 - Classical baselines (XGBoost/LightGBM/CatBoost/RF) trail noticeably, highlighting Orion-MSP’s robustness across diverse tabular tasks.
@@ -390,6 +396,7 @@ OrionMSP excels on narrow and stays strong on medium width, while TabPFN dominat
 shine on wide feature spaces.
 
 ## Usage
+### Direct (OrionMSP Python package)
 
 ```python
 from orion_msp.sklearn import OrionMSPClassifier
@@ -402,9 +409,36 @@ predictions = clf.predict(X_test)
 
 This code will automatically download the pre-trained model from Hugging Face and use a GPU if available.
 
+### Via TabTune (unified TFM library)
+
+```python
+from tabtune import TabularPipeline
+
+pipeline = TabularPipeline(
+    model_name="OrionMSP",          # use OrionMSP through TabTune
+    tuning_strategy="inference",    # zero-shot / in-context mode
+    tuning_params={"device": "cuda"}  # or "cpu"
+)
+
+pipeline.fit(X_train, y_train)
+predictions = pipeline.predict(X_test)
+```
+
+When used through TabTune, the OrionMSP weights are automatically downloaded from this Hugging Face repository on first use, and TabTune handles model-aware preprocessing for you.
+
+
 ## Installation
 
-### From the source
+### Via TabTune (recommended if you want multiple tabular FMs)
+
+```bash
+pip install tabtune
+```
+
+This installs TabTune and its built-in OrionMSP integration; no separate orion-msp install is required.
+
+
+### From the OrionMSP source
 #### Option 1: From the local clone
 
 ```bash