testing new commands in antora

modules/chapter3/pages/section1.adoc

@@ -331,13 +331,13 @@ Navigate to the Data Science Project section of the OpenShift AI Console /Dashbo
 
  . Select the WorkBench button, then create workbench
 
- .. Name:  ollama-model
+ .. Name:  `ollama-model`
 
- .. Notebook Image:  Minimal Python
+ .. Notebook Image:  `Minimal Python`
 
  .. Leave the remianing options default
 
- .. Optionally, scroll to the bottom, check the Use data connection box
+ .. Optionally, scroll to the bottom, check the `Use data connection box`
 
  .. Select *storage* from the dropdown to attach the storage bucket to the workbench.  
 

modules/chapter3/pages/section2.adoc

@@ -45,20 +45,19 @@ Before we continue we need to perform the following additional step. As mentione
 
 We will need to obtain the endpoint from the OpenShift AI model serving console. I usually just paste the text below into a cell in the Jupyter Notebook and paste the url in the code block from there.
 
-```yaml
+[source, yaml]
+----
 curl https://your-endpoint/api/pull \
     -k \
     -H "Content-Type: application/json" \
     -d '{"name": "mistral"}'
+----
 
-
-```
-. Next copy the entire code snippet, and open the OpenShift Dashboard.
-. At the top right of the dashboard, locate the ">_" and select it. 
-. This will open the terminal window at the bottom of the dashboard.
-. Click on the Start button in the terminal window, wait for the bash..$ prompt to appear
-. Past the modified code block into the window and press enter.
-
+ . Next copy the entire code snippet, and open the OpenShift Dashboard.
+ . At the top right of the dashboard, locate the ">_" and select it. 
+ . This will open the terminal window at the bottom of the dashboard.
+ . Click on the Start button in the terminal window, wait for the bash..$ prompt to appear
+ . Past the modified code block into the window and press enter.
 
 The message:  *status: pulling manifest* should appear.  This begins the model downloading process.