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README.md

@@ -32,7 +32,7 @@ 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 | 1.386 ms | 0 - 8 MB | FP16 | NPU |  [QuickSRNetMedium.tflite](https://huggingface.co/qualcomm/QuickSRNetMedium/blob/main/QuickSRNetMedium.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.385 ms | 0 - 1 MB | FP16 | NPU |  [QuickSRNetMedium.tflite](https://huggingface.co/qualcomm/QuickSRNetMedium/blob/main/QuickSRNetMedium.tflite) 
 | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.998 ms | 0 - 7 MB | FP16 | NPU |  [QuickSRNetMedium.so](https://huggingface.co/qualcomm/QuickSRNetMedium/blob/main/QuickSRNetMedium.so) 
 
 
@@ -90,23 +90,6 @@ device. This script does the following:
 python -m qai_hub_models.models.quicksrnetmedium.export
 ```
 
-```
-Profile Job summary of QuickSRNetMedium
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.90 ms
-Estimated Peak Memory Range: 0.02-18.70 MB
-Compute Units: NPU (14),CPU (3) | Total (17)
-
-Profile Job summary of QuickSRNetMedium
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.65 ms
-Estimated Peak Memory Range: 0.20-14.70 MB
-Compute Units: NPU (17) | Total (17)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/QuickSRNetMedium/export.py)