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 | 0.212 ms | 0 - 1 MB | FP16 | NPU |  [SqueezeNet-1_1.tflite](https://huggingface.co/qualcomm/SqueezeNet-1_1/blob/main/SqueezeNet-1_1.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.28 ms | 0 - 12 MB | FP16 | NPU |  [SqueezeNet-1_1.so](https://huggingface.co/qualcomm/SqueezeNet-1_1/blob/main/SqueezeNet-1_1.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.225 ms | 0 - 1 MB | FP16 | NPU |  [SqueezeNet-1_1.tflite](https://huggingface.co/qualcomm/SqueezeNet-1_1/blob/main/SqueezeNet-1_1.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.278 ms | 0 - 51 MB | FP16 | NPU |  [SqueezeNet-1_1.so](https://huggingface.co/qualcomm/SqueezeNet-1_1/blob/main/SqueezeNet-1_1.so) 
 
 
 ## Installation
@@ -97,16 +97,16 @@ python -m qai_hub_models.models.squeezenet1_1.export
 ```
 Profile Job summary of SqueezeNet-1_1
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.21 ms
-Estimated Peak Memory Range: 0.02-1.37 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.18 ms
+Estimated Peak Memory Range: 0.01-20.67 MB
 Compute Units: NPU (39) | Total (39)
 
 Profile Job summary of SqueezeNet-1_1
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.28 ms
-Estimated Peak Memory Range: 0.02-11.89 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.20 ms
+Estimated Peak Memory Range: 0.59-27.09 MB
 Compute Units: NPU (69) | Total (69)
 
 
@@ -226,7 +226,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of SqueezeNet-1_1 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
 * [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360)