Addressed comments

Author: ahg-g

pkg/manifests/inferencemodel.yaml

@@ -1,18 +1,11 @@
 apiVersion: inference.networking.x-k8s.io/v1alpha1
 kind: InferenceModel
 metadata:
-  labels:
-    app.kubernetes.io/name: api
-    app.kubernetes.io/managed-by: kustomize
   name: inferencemodel-sample
 spec:
   modelName: tweet-summary
   criticality: Critical
   poolRef:
-    # this is the default val:
-    group: inference.networking.x-k8s.io
-    # this is the default val:
-    kind: InferencePool
     name: vllm-llama2-7b-pool
   targetModels:
   - name: tweet-summary-1

pkg/manifests/vllm/deployment.yaml

@@ -88,7 +88,7 @@ spec:
           env: 
             - name: DYNAMIC_LORA_ROLLOUT_CONFIG
               value: "/config/configmap.yaml"
-          volumeMounts: # DO NOT USE subPath
+          volumeMounts: # DO NOT USE subPath, dynamic configmap updates don't work on subPaths
           - name: config-volume
             mountPath:  /config
       restartPolicy: Always

site-src/guides/adapter-rollout.md

@@ -2,7 +2,7 @@
 
 The goal of this guide is to demonstrate how to rollout a new adapter version.
 
-## **Requirements**
+## **Prerequisites**
 
 Follow the steps in the [main guide](index.md)
 
@@ -52,20 +52,27 @@ Modify the InferenceModel to configure a canary rollout with traffic splitting.
 
 
 ```bash
-   kubectl edit configmap tweet-summary
+   kubectl edit inferencemodel tweet-summary
 ```
 
-Change the InferenceModel to match the following:
+Change the targetModels list in InferenceModel to a the following:
 
 
 ```yaml
-model:
-    name: tweet-summary
-    targetModels:
-    targetModelName: tweet-summary-1
-            weight: 90
-    targetModelName: tweet-summary-2
-            weight: 10
+apiVersion: inference.networking.x-k8s.io/v1alpha1
+kind: InferenceModel
+metadata:
+  name: inferencemodel-sample
+spec:
+  modelName: tweet-summary
+  criticality: Critical
+  poolRef:
+    name: vllm-llama2-7b-pool
+  targetModels:
+  - name: tweet-summary-1
+    weight: 90
+  - name: tweet-summary-2
+    weight: 10
 
 ```
 

site-src/guides/index.md

@@ -2,7 +2,7 @@
 
 This quickstart guide is intended for engineers familiar with k8s and model servers (vLLM in this instance). The goal of this guide is to get a first, single InferencePool up and running! 
 
-## **Requirements**
+## **Prerequisites**
  - Envoy Gateway [v1.2.1](https://gateway.envoyproxy.io/docs/install/install-yaml/#install-with-yaml) or higher
  - A cluster with:
    - Support for Services of type `LoadBalancer`. (This can be validated by ensuring your Envoy Gateway is up and running). For example, with Kind,