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 | 7.13 ms | 1 - 23 MB | FP16 | 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 | 7.127 ms | 1 - 2 MB | INT8 | NPU |  [FFNet-54S-Quantized.tflite](https://huggingface.co/qualcomm/FFNet-54S-Quantized/blob/main/FFNet-54S-Quantized.tflite) 
 
 
 ## Installation
@@ -96,9 +96,9 @@ python -m qai_hub_models.models.ffnet_54s_quantized.export
 ```
 Profile Job summary of FFNet-54S-Quantized
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 7.13 ms
-Estimated Peak Memory Range: 0.61-22.86 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 5.14 ms
+Estimated Peak Memory Range: 0.02-68.36 MB
 Compute Units: NPU (118) | Total (118)
 
 
@@ -204,7 +204,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of FFNet-54S-Quantized can be found
   [here](https://github.com/Qualcomm-AI-research/FFNet/blob/master/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
 * [Simple and Efficient Architectures for Semantic Segmentation](https://arxiv.org/abs/2206.08236)