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README.md

@@ -34,12 +34,15 @@ More details on model performance across various devices, can be found
   - Model size (CLIPImageEncoder): 437 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 | 13.251 ms | 0 - 3 MB | FP16 | NPU |  [CLIPTextEncoder.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 126.637 ms | 0 - 4 MB | FP16 | NPU |  [CLIPImageEncoder.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 7.849 ms | 0 - 24 MB | FP16 | NPU |  [CLIPTextEncoder.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.so) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 50.638 ms | 0 - 61 MB | FP16 | NPU |  [CLIPImageEncoder.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 13.293 ms | 0 - 3 MB | FP16 | NPU |  [CLIPTextEncoder.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 126.539 ms | 0 - 261 MB | FP16 | NPU |  [CLIPImageEncoder.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 7.81 ms | 0 - 30 MB | FP16 | NPU |  [CLIPTextEncoder.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 50.274 ms | 0 - 63 MB | FP16 | NPU |  [CLIPImageEncoder.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.so) 
+
 
 
 ## Installation
@@ -101,22 +104,24 @@ python -m qai_hub_models.models.openai_clip.export
 Profile Job summary of CLIPTextEncoder
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 8.46 ms
-Estimated Peak Memory Range: 0.14-0.14 MB
+Estimated Inference Time: 8.43 ms
+Estimated Peak Memory Range: 0.15-0.15 MB
 Compute Units: NPU (377) | Total (377)
 
 Profile Job summary of CLIPImageEncoder
 --------------------------------------------------
 Device: Snapdragon X Elite CRD (11)
-Estimated Inference Time: 48.88 ms
+Estimated Inference Time: 48.61 ms
 Estimated Peak Memory Range: 0.57-0.57 MB
 Compute Units: NPU (369) | Total (369)
 
 
 ```
+
+
 ## How does this work?
 
-This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/OpenAI-Clip/export.py)
+This [export script](https://aihub.qualcomm.com/models/openai_clip/qai_hub_models/models/OpenAI-Clip/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:
 
@@ -194,6 +199,7 @@ AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
 
 
 
+
 ## Deploying compiled model to Android
 
 
@@ -215,7 +221,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of OpenAI-Clip can be found
   [here](https://github.com/openai/CLIP/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
 * [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020)