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Upload README.md with huggingface_hub

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@@ -33,7 +33,7 @@ More details on model performance across various devices, can be found
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  | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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  | ---|---|---|---|---|---|---|---|
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 884.463 ms | 142 - 146 MB | FP32 | CPU | [HuggingFace-WavLM-Base-Plus.tflite](https://huggingface.co/qualcomm/HuggingFace-WavLM-Base-Plus/blob/main/HuggingFace-WavLM-Base-Plus.tflite)
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  ## Installation
@@ -91,16 +91,6 @@ device. This script does the following:
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  python -m qai_hub_models.models.huggingface_wavlm_base_plus.export
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  ```
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- ```
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- Profile Job summary of HuggingFace-WavLM-Base-Plus
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- --------------------------------------------------
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- Device: QCS8550 (Proxy) (12)
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- Estimated Inference Time: 928.77 ms
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- Estimated Peak Memory Range: 143.20-150.90 MB
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- Compute Units: CPU (811) | Total (811)
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-
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- ```
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  ## How does this work?
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  This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/HuggingFace-WavLM-Base-Plus/export.py)
 
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  | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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  | ---|---|---|---|---|---|---|---|
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+ | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 938.575 ms | 124 - 137 MB | FP32 | CPU | [HuggingFace-WavLM-Base-Plus.tflite](https://huggingface.co/qualcomm/HuggingFace-WavLM-Base-Plus/blob/main/HuggingFace-WavLM-Base-Plus.tflite)
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  ## Installation
 
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  python -m qai_hub_models.models.huggingface_wavlm_base_plus.export
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  ```
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  ## How does this work?
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  This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/HuggingFace-WavLM-Base-Plus/export.py)