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README.md

@@ -34,9 +34,12 @@ More details on model performance across various devices, can be found
   - Model size: 21.7 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 | 6.73 ms | 1 - 4 MB | FP16 | NPU |  [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 6.65 ms | 0 - 26 MB | FP16 | NPU |  [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) 
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 ## Installation
@@ -93,9 +96,11 @@ device. This script does the following:
 python -m qai_hub_models.models.ddrnet23_slim.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/DDRNet23-Slim/export.py)
+This [export script](https://aihub.qualcomm.com/models/ddrnet23_slim/qai_hub_models/models/DDRNet23-Slim/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:
 
@@ -172,6 +177,7 @@ spot check the output with expected output.
 AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
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 ## Run demo on a cloud-hosted device
 
 You can also run the demo on-device.
@@ -208,7 +214,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of DDRNet23-Slim can be found
   [here](https://github.com/chenjun2hao/DDRNet.pytorch/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://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
 
 ## References
 * [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://arxiv.org/abs/2101.06085)