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README.md

@@ -31,7 +31,7 @@ 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 | 15.966 ms | 6 - 13 MB | FP16 | NPU |  [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 15.866 ms | 6 - 10 MB | FP16 | NPU |  [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) 
 
 
 ## Installation
@@ -92,9 +92,9 @@ python -m qai_hub_models.models.litehrnet.export
 ```
 Profile Job summary of LiteHRNet
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 15.97 ms
-Estimated Peak Memory Range: 6.26-12.88 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 10.70 ms
+Estimated Peak Memory Range: 0.02-68.35 MB
 Compute Units: NPU (1226),CPU (10) | Total (1236)
 
 
@@ -214,7 +214,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of LiteHRNet can be found
   [here](https://github.com/HRNet/Lite-HRNet/blob/hrnet/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
 * [Lite-HRNet: A Lightweight High-Resolution Network](https://arxiv.org/abs/2104.06403)