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README.md

@@ -32,10 +32,13 @@ More details on model performance across various devices, can be found
   - Model size (TrOCRDecoder): 146 MB
 
 
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 | 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 | 149.72 ms | 7 - 10 MB | FP16 | NPU |  [TrOCREncoder.tflite](https://huggingface.co/qualcomm/TrOCR/blob/main/TrOCREncoder.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.734 ms | 0 - 2 MB | FP16 | NPU |  [TrOCRDecoder.tflite](https://huggingface.co/qualcomm/TrOCR/blob/main/TrOCRDecoder.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 148.428 ms | 6 - 9 MB | FP16 | NPU |  [TrOCREncoder.tflite](https://huggingface.co/qualcomm/TrOCR/blob/main/TrOCREncoder.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.732 ms | 0 - 2 MB | FP16 | NPU |  [TrOCRDecoder.tflite](https://huggingface.co/qualcomm/TrOCR/blob/main/TrOCRDecoder.tflite) 
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 ## Installation
@@ -93,9 +96,11 @@ device. This script does the following:
 python -m qai_hub_models.models.trocr.export
 ```
 
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 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/TrOCR/export.py)
+This [export script](https://aihub.qualcomm.com/models/trocr/qai_hub_models/models/TrOCR/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:
 
@@ -173,6 +178,7 @@ AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
 
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 ## Deploying compiled model to Android
 
 
@@ -194,7 +200,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of TrOCR can be found
   [here](https://github.com/microsoft/unilm/blob/master/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://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
 ## References
 * [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282)