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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 | 30.809 ms | 0 - 72 MB | FP16 | NPU |  [Swin-Tiny.tflite](https://huggingface.co/qualcomm/Swin-Tiny/blob/main/Swin-Tiny.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 31.313 ms | 0 - 3 MB | FP16 | NPU |  [Swin-Tiny.tflite](https://huggingface.co/qualcomm/Swin-Tiny/blob/main/Swin-Tiny.tflite) 
 
 
 ## Installation
@@ -96,10 +96,10 @@ python -m qai_hub_models.models.swin_tiny.export
 ```
 Profile Job summary of Swin-Tiny
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 30.81 ms
-Estimated Peak Memory Range: 0.00-72.42 MB
-Compute Units: NPU (1059) | Total (1059)
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 20.72 ms
+Estimated Peak Memory Range: 0.05-261.80 MB
+Compute Units: NPU (859) | Total (859)
 
 
 ```
@@ -218,7 +218,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of Swin-Tiny 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
 * [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)