Upload README.md with huggingface_hub

README.md

@@ -36,7 +36,7 @@ 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 | 8.368 ms | 1 - 2 MB | INT8 | NPU |  [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 8.383 ms | 1 - 38 MB | INT8 | NPU |  [FFNet-78S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-78S-Quantized/blob/main/FFNet-78S-Quantized.tflite) 
 
 
 ## Installation
@@ -44,10 +44,11 @@ More details on model performance across various devices, can be found
 This model can be installed as a Python package via pip.
 
 ```bash
-pip install qai-hub-models
+pip install "qai-hub-models[ffnet_78s_quantized]"
 ```
 
 
+
 ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
 
 Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
@@ -97,8 +98,8 @@ python -m qai_hub_models.models.ffnet_78s_quantized.export
 Profile Job summary of FFNet-78S-Quantized
 --------------------------------------------------
 Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 6.09 ms
-Estimated Peak Memory Range: 0.02-80.31 MB
+Estimated Inference Time: 5.98 ms
+Estimated Peak Memory Range: 0.03-83.11 MB
 Compute Units: NPU (154) | Total (154)