links updated

Author: santiagxf

articles/machine-learning/reference-model-inference-api.md

@@ -52,7 +52,7 @@ The API is compatible with Azure OpenAI model deployments.
 
 ## Capabilities
 
-The following section describes some of the capabilities the API exposes. For a full specification of the API, view the [OpenAPI definition](https://github.com/Azure/azureai-model-inference-api).
+The following section describes some of the capabilities the API exposes. For a full specification of the API, view the [reference section](reference-model-inference-info.md).
 
 ### Modalities
 
@@ -64,18 +64,21 @@ The API indicates how developers can consume predictions for the following modal
 * [Chat completions](reference-model-inference-chat-completions.md): Creates a model response for the given chat conversation.
 * [Image embeddings](reference-model-inference-images-embeddings.md): Creates an embedding vector representing the input text and image.
 
-### Models with disparate set of capabilities
+### Extensibility
 
-The Azure AI Model Inference API indicates a general set of capabilities but each of the models can decide to implement them or not. A specific error is returned on those cases where the model can't support a specific parameter.
+The Azure AI Model Inference API specifies a set of modalities and parameters that models can subscribe to. However, some models may have further capabilities that the ones the API indicates. On those cases, the API allows the developer to pass them as extra parameters in the payload.
 
-The following example shows the response for a chat completion request indicating the parameter `reponse_format` and asking for a reply in `JSON` format. In the example, since the model doesn't support such capability an error 422 is returned to the user. 
+By setting a header `extra-parameters: allow`, the API will attempt to pass any unknown parameter directly to the underlying model. If the model can handle that parameter, the request completes.
+
+The following example shows a request passing the parameter `safe_prompt` supported by Mistral-Large, which isn't specified in the Azure AI Model Inference API:
 
 __Request__
 
 ```HTTP/1.1
 POST /chat/completions?api-version=2024-05-01-preview
 Authorization: Bearer <bearer-token>
 Content-Type: application/json
+extra-parameters: allow
 ```
 
 ```JSON
@@ -92,42 +95,26 @@ Content-Type: application/json
     ],
     "temperature": 0,
     "top_p": 1,
-    "response_format": { "type": "json_object" },
-}
-```
-
-__Response__
-
-```JSON
-{
-    "status": 422,
-    "code": "parameter_not_supported",
-    "detail": {
-        "loc": [ "body", "response_format" ],
-        "input": "json_object"
-    },
-    "message": "One of the parameters contain invalid values."
+    "response_format": { "type": "text" },
+    "safe_prompt": true
 }
 ```
 
 > [!TIP]
-> You can inspect the property `details.loc` to understand the location of the offending parameter and `details.input` to see the value that was passed in the request.
-
-### Extensibility
+> Alternatively, you can set `extra-parameters: drop` to drop any unknown parameter in the request. Use this capability in case you happen to be sending requests with extra parameters that you know the model won't support but you want the request to completes anyway. A typical example of this is indicating `seed` parameter.
 
-The Azure AI Model Inference API specifies a set of modalities and parameters that models can subscribe to. However, some models may have further capabilities that the ones the API indicates. On those cases, the API allows the developer to pass them as extra parameters in the payload.
+### Models with disparate set of capabilities
 
-By setting a header `extra-parameters: allow`, the API will attempt to pass any unknown parameter directly to the underlying model. If the model can handle that parameter, the request completes.
+The Azure AI Model Inference API indicates a general set of capabilities but each of the models can decide to implement them or not. A specific error is returned on those cases where the model can't support a specific parameter.
 
-The following example shows a request passing the parameter `safe_prompt` supported by Mistral-Large, which isn't specified in the Azure AI Model Inference API:
+The following example shows the response for a chat completion request indicating the parameter `reponse_format` and asking for a reply in `JSON` format. In the example, since the model doesn't support such capability an error 422 is returned to the user. 
 
 __Request__
 
 ```HTTP/1.1
 POST /chat/completions?api-version=2024-05-01-preview
 Authorization: Bearer <bearer-token>
 Content-Type: application/json
-extra-parameters: allow
 ```
 
 ```JSON
@@ -144,13 +131,27 @@ extra-parameters: allow
     ],
     "temperature": 0,
     "top_p": 1,
-    "response_format": { "type": "text" },
-    "safe_prompt": true
+    "response_format": { "type": "json_object" },
+}
+```
+
+__Response__
+
+```JSON
+{
+    "status": 422,
+    "code": "parameter_not_supported",
+    "detail": {
+        "loc": [ "body", "response_format" ],
+        "input": "json_object"
+    },
+    "message": "One of the parameters contain invalid values."
 }
 ```
 
 > [!TIP]
-> Alternatively, you can set `extra-parameters: drop` to drop any unknown parameter in the request. Use this capability in case you happen to be sending requests with extra parameters that you know the model won't support but you want the request to completes anyway. A typical example of this is indicating `seed` parameter.
+> You can inspect the property `details.loc` to understand the location of the offending parameter and `details.input` to see the value that was passed in the request.
+
 
 ## Getting started