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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 | 135.762 ms | 0 - 3 MB | FP16 | NPU |  [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 136.11 ms | 0 - 4 MB | FP16 | NPU |  [VIT.tflite](https://huggingface.co/qualcomm/VIT/blob/main/VIT.tflite) 
 
 
 ## Installation
@@ -96,9 +96,9 @@ python -m qai_hub_models.models.vit.export
 ```
 Profile Job summary of VIT
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 135.76 ms
-Estimated Peak Memory Range: 0.14-3.18 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 100.29 ms
+Estimated Peak Memory Range: 0.16-382.58 MB
 Compute Units: NPU (557) | Total (557)
 
 
@@ -218,7 +218,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of VIT 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
 * [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929)