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README.md

@@ -34,8 +34,8 @@ 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 | 29.26 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-78S.tflite](https://huggingface.co/qualcomm/FFNet-78S/blob/main/FFNet-78S.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 23.452 ms | 24 - 43 MB | FP16 | NPU |  [FFNet-78S.so](https://huggingface.co/qualcomm/FFNet-78S/blob/main/FFNet-78S.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 29.177 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-78S.tflite](https://huggingface.co/qualcomm/FFNet-78S/blob/main/FFNet-78S.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 23.42 ms | 24 - 46 MB | FP16 | NPU |  [FFNet-78S.so](https://huggingface.co/qualcomm/FFNet-78S/blob/main/FFNet-78S.so) 
 
 
 ## Installation
@@ -93,23 +93,6 @@ device. This script does the following:
 python -m qai_hub_models.models.ffnet_78s.export
 ```
 
-```
-Profile Job summary of FFNet-78S
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 21.32 ms
-Estimated Peak Memory Range: 1.16-129.65 MB
-Compute Units: NPU (149) | Total (149)
-
-Profile Job summary of FFNet-78S
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 17.80 ms
-Estimated Peak Memory Range: 217.90-292.70 MB
-Compute Units: NPU (235) | Total (235)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/FFNet-78S/export.py)