docs(python): rpt 1.6 updated

docs-python/examples/gen-ai-hub.mdx

@@ -351,15 +351,15 @@ response = embedding_model.embed_query('Every decoding is another encoding.')
 print(response)
 ```
 
-# SAP RPT-1 Models
+# SAP RPT Models
 
-SAP-RPT-1 is a relational pretrained transformer for use on relational and structured data, developed and maintained by SAP. Relational Foundation Models (RFMs) are large-scale machine learning models designed to understand, process, and make predictions on tabular and relational data.
+SAP-RPT is a relational pretrained transformer for use on relational and structured data, developed and maintained by SAP. Relational Foundation Models (RFMs) are large-scale machine learning models designed to understand, process, and make predictions on tabular and relational data.
 
-SAP-RPT-1 is a table-native model aiming to achieve the highest prediction quality and lowest error rates for predictions on tabular business data. It's pretrained and doesn't need additional training or fine-tuning steps. It solves predictive tasks such as classification and regression out-of-the-box via in-context learning. Due to its table-native architecture, prediction quality on enterprise data is typically very high, ahead of state-of-the-art narrow AI models and LLMs employed for such tasks.
+SAP-RPT is a table-native model aiming to achieve the highest prediction quality and lowest error rates for predictions on tabular business data. It's pretrained and doesn't need additional training or fine-tuning steps. It solves predictive tasks such as classification and regression out-of-the-box via in-context learning. Due to its table-native architecture, prediction quality on enterprise data is typically very high, ahead of state-of-the-art narrow AI models and LLMs employed for such tasks.
 
 You can get predictions directly by the native SDK client.
 
-For detailed information visit the [SAP Help Portal](https://help.sap.com/docs/sap-ai-core/generative-ai/example-payloads-for-inferencing-sap-rpt-1?locale=en-US).
+For detailed information visit the [SAP Help Portal](https://help.sap.com/docs/sap-ai-core/generative-ai/sap-rpt-1-6?locale=en-US).
 
 ### Regression
 
@@ -379,15 +379,15 @@ rows_regression = [
 client = RPTClient()
 body = RPTRequest(
     prediction_config=PredictionConfig(
-        target_columns=[TargetColumn(name="DISCOUNT_RATE", task_type="regression")]
+        target_columns=[TargetColumn(name="DISCOUNT_RATE", prediction_placeholder="[PREDICT]", task_type="regression")]
     ),
     rows=rows_regression
 )
-response = client.predict(body=body, model_name="sap-rpt-1-small")
+response = client.predict(body=body, model_name="sap-rpt-1.6")
 print(response.predictions)
 
 # With model_version
-response = client.predict(body=body, model_name="sap-rpt-1-small", model_version="latest")
+response = client.predict(body=body, model_name="sap-rpt-1.6", model_version="latest")
 print(response.predictions)
 ```
 
@@ -419,7 +419,7 @@ example_request_by_columns_dict = {
     }
 }
 
-response = client.predict(body=example_request_by_columns_dict, model_name="sap-rpt-1-small")
+response = client.predict(body=example_request_by_columns_dict, model_name="sap-rpt-1.6")
 print(response.predictions)
 ```
 
@@ -428,5 +428,5 @@ print(response.predictions)
 The `RPTClient` also supports asynchronous calls via the `apredict` method.
 
 ```python
-await client.apredict(body=example_request_by_columns_dict, model_name="sap-rpt-1-small")
+await client.apredict(body=example_request_by_columns_dict, model_name="sap-rpt-1.6")
 ```