Upload README.md with huggingface_hub

README.md

@@ -39,7 +39,7 @@ More details on model performance across various devices, can be found
 
 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
 | ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 4.719 ms | 2 - 10 MB | INT8 | NPU |  [FFNet-54S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-54S-Quantized/blob/main/FFNet-54S-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 4.739 ms | 1 - 6 MB | INT8 | NPU |  [FFNet-54S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-54S-Quantized/blob/main/FFNet-54S-Quantized.tflite) 
 
 
 
@@ -102,6 +102,20 @@ python -m qai_hub_models.models.ffnet_54s_quantized.export
 
 
 
+## Run demo on a cloud-hosted device
+
+You can also run the demo on-device.
+
+```bash
+python -m qai_hub_models.models.ffnet_54s_quantized.demo --on-device
+```
+
+**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
+environment, please add the following to your cell (instead of the above).
+```
+%run -m qai_hub_models.models.ffnet_54s_quantized.demo -- --on-device
+```
+
 
 ## Deploying compiled model to Android