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README.md

@@ -190,11 +190,17 @@ After compiling models from step 1. Models can be profiled model on-device using
 provisioned in the cloud.  Once the job is submitted, you can navigate to a
 provided job URL to view a variety of on-device performance metrics.
 ```python
-profile_job = hub.submit_profile_job(
-        model=target_model,
+
+encoder_profile_job = hub.submit_profile_job(
+        model=encoder_target_model,
+        device=device,
+        )       
+
+decoder_profile_job = hub.submit_profile_job(
+        model=decoder_target_model,
         device=device,
-        )
-        
+        )       
+
 ```
 
 Step 3: **Verify on-device accuracy**
@@ -202,13 +208,20 @@ Step 3: **Verify on-device accuracy**
 To verify the accuracy of the model on-device, you can run on-device inference
 on sample input data on the same cloud hosted device.
 ```python
-input_data = torch_model.sample_inputs()
-    inference_job = hub.submit_inference_job(
-        model=target_model,
+encoder_input_data = encoder_model.sample_inputs()
+encoder_inference_job = hub.submit_inference_job(
+        model=encoder_target_model,
+        device=device,
+        inputs=encoder_input_data,
+    )
+    encoder_inference_job.download_output_data()
+decoder_input_data = decoder_model.sample_inputs()
+decoder_inference_job = hub.submit_inference_job(
+        model=decoder_target_model,
         device=device,
-        inputs=input_data,
+        inputs=decoder_input_data,
     )
-    on_device_output = inference_job.download_output_data()
+    decoder_inference_job.download_output_data()
 
 ```
 With the output of the model, you can compute like PSNR, relative errors or