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README.md

@@ -35,7 +35,7 @@ More details on model performance across various devices, can be found
 
 | 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 | 920.916 ms | 141 - 148 MB | FP32 | CPU |  [HuggingFace-WavLM-Base-Plus.tflite](https://huggingface.co/qualcomm/HuggingFace-WavLM-Base-Plus/blob/main/HuggingFace-WavLM-Base-Plus.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 982.866 ms | 142 - 145 MB | FP32 | CPU |  [HuggingFace-WavLM-Base-Plus.tflite](https://huggingface.co/qualcomm/HuggingFace-WavLM-Base-Plus/blob/main/HuggingFace-WavLM-Base-Plus.tflite) 
 
 
 
@@ -97,9 +97,9 @@ python -m qai_hub_models.models.huggingface_wavlm_base_plus.export
 ```
 Profile Job summary of HuggingFace-WavLM-Base-Plus
 --------------------------------------------------
-Device: QCS8550 (Proxy) (12)
-Estimated Inference Time: 932.00 ms
-Estimated Peak Memory Range: 142.46-146.71 MB
+Device: SA8775 (Proxy) (13)
+Estimated Inference Time: 915.09 ms
+Estimated Peak Memory Range: 141.95-154.50 MB
 Compute Units: CPU (811) | Total (811)
 
 
@@ -125,7 +125,6 @@ from qai_hub_models.models.huggingface_wavlm_base_plus import Model
 
 # Load the model
 torch_model = Model.from_pretrained()
-torch_model.eval()
 
 # Device
 device = hub.Device("Samsung Galaxy S23")