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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 | 0.37 ms | 0 - 2 MB | FP16 | NPU |  [MNASNet05.tflite](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.362 ms | 0 - 115 MB | FP16 | NPU |  [MNASNet05.so](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.8 ms | 0 - 2 MB | FP16 | NPU |  [MNASNet05.tflite](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.848 ms | 1 - 5 MB | FP16 | NPU |  [MNASNet05.so](https://huggingface.co/qualcomm/MNASNet05/blob/main/MNASNet05.so) 
 
 
 ## Installation
@@ -94,23 +94,6 @@ device. This script does the following:
 python -m qai_hub_models.models.mnasnet05.export
 ```
 
-```
-Profile Job summary of MNASNet05
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.28 ms
-Estimated Peak Memory Range: 0.02-42.54 MB
-Compute Units: NPU (69) | Total (69)
-
-Profile Job summary of MNASNet05
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.26 ms
-Estimated Peak Memory Range: 0.59-35.64 MB
-Compute Units: NPU (101) | Total (101)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/MNASNet05/export.py)