Upload README.md with huggingface_hub

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 | 50.305 ms | 0 - 3 MB | FP16 | NPU |  [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 46.059 ms | 0 - 8 MB | FP16 | NPU |  [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) 
 
 
 ## Installation
@@ -93,16 +93,6 @@ device. This script does the following:
 python -m qai_hub_models.models.swin_small.export
 ```
 
-```
-Profile Job summary of Swin-Small
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 32.98 ms
-Estimated Peak Memory Range: 0.04-457.50 MB
-Compute Units: NPU (1609) | Total (1609)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Swin-Small/export.py)