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README.md

@@ -31,10 +31,12 @@ More details on model performance across various devices, can be found
   - Model size: 45.1 MB
 
 
+
+
 | 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 | 8.641 ms | 8 - 10 MB | FP16 | NPU |  [FastSam-S.tflite](https://huggingface.co/qualcomm/FastSam-S/blob/main/FastSam-S.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 8.21 ms | 5 - 20 MB | FP16 | NPU |  [FastSam-S.so](https://huggingface.co/qualcomm/FastSam-S/blob/main/FastSam-S.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 8.7 ms | 8 - 38 MB | FP16 | NPU |  [FastSam-S.tflite](https://huggingface.co/qualcomm/FastSam-S/blob/main/FastSam-S.tflite) 
+
 
 
 ## Installation
@@ -92,19 +94,11 @@ device. This script does the following:
 python -m qai_hub_models.models.fastsam_s.export
 ```
 
-```
-Profile Job summary of FastSam-S
---------------------------------------------------
-Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 9.29 ms
-Estimated Peak Memory Range: 4.71-4.71 MB
-Compute Units: NPU (286) | Total (286)
 
 
-```
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/FastSam-S/export.py)
+This [export script](https://aihub.qualcomm.com/models/fastsam_s/qai_hub_models/models/FastSam-S/export.py)
 leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
 on-device. Lets go through each step below in detail:
 
@@ -181,6 +175,7 @@ spot check the output with expected output.
 AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
+
 ## Run demo on a cloud-hosted device
 
 You can also run the demo on-device.
@@ -217,7 +212,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of FastSam-S can be found
   [here](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/LICENSE).
-- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
+- The license for the compiled assets for on-device deployment can be found [here](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/LICENSE)
 
 ## References
 * [Fast Segment Anything](https://arxiv.org/abs/2306.12156)