Update README.md

README.md

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----
-license: other
-license_name: nvclv1
-license_link: LICENSE
----
+---
+license: other
+license_name: nvclv1
+license_link: LICENSE
+datasets:
+- ILSVRC/imagenet-1k
+pipeline_tag: image-classification
+---
+
+
+[**MambaVision: A Hybrid Mamba-Transformer Vision Backbone**](https://arxiv.org/abs/2407.08083).
+
+### Model Overview
+
+We introduce a novel mixer block by creating a symmetric path without SSM to enhance the modeling of global context. MambaVision has a hierarchical architecture that employs both self-attention and mixer blocks. 
+
+
+### Model Performance
+
+MambaVision demonstrates a strong performance by achieving a new SOTA Pareto-front in
+terms of Top-1 accuracy and throughput. 
+
+<p align="center">
+<img src="https://github.com/NVlabs/MambaVision/assets/26806394/79dcf841-3966-4b77-883d-76cd5e1d4320" width=42% height=42% 
+class="center">
+</p>
+
+
+### Model Usage
+
+You must first login into HuggingFace to pull the model:
+
+```Bash
+huggingface-cli login
+```
+
+The model can be simply used according to:
+
+```Python
+access_token = "<YOUR ACCESS TOKEN"
+model = AutoModel.from_pretrained("nvidia/MambaVision-S-1K", trust_remote_code=True)
+```
+
+
+### License: 
+
+[NVIDIA Source Code License-NC](https://huggingface.co/nvidia/MambaVision-S-1K/blob/main/LICENSE)
+
+