Upload README.md with huggingface_hub

README.md

@@ -36,8 +36,8 @@ 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 | 0.37 ms | 0 - 9 MB | FP16 | NPU |  [MNASNet05.tflite](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.367 ms | 0 - 35 MB | FP16 | NPU |  [MNASNet05.so](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.383 ms | 0 - 2 MB | FP16 | NPU |  [MNASNet05.tflite](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.358 ms | 1 - 5 MB | FP16 | NPU |  [MNASNet05.so](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.so) 
 
 
 ## Installation
@@ -97,16 +97,16 @@ python -m qai_hub_models.models.mnasnet05.export
 ```
 Profile Job summary of MNASNet05
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.37 ms
-Estimated Peak Memory Range: 0.01-8.54 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.28 ms
+Estimated Peak Memory Range: 0.01-42.05 MB
 Compute Units: NPU (69) | Total (69)
 
 Profile Job summary of MNASNet05
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.37 ms
-Estimated Peak Memory Range: 0.19-34.65 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.26 ms
+Estimated Peak Memory Range: 0.00-32.08 MB
 Compute Units: NPU (102) | Total (102)
 
 
@@ -226,7 +226,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of MNASNet05 can be found
   [here](https://github.com/pytorch/vision/blob/main/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf).
+- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
 
 ## References
 * [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626)