Merge pull request #1939 from jasonrandrews/review

Review and remove draft from ONNX with Phi-3.5 on Cobalt 100

Author: jasonrandrews

content/learning-paths/servers-and-cloud-computing/onnx/_index.md

@@ -1,11 +1,5 @@
 ---
-title: Deploy Phi-3.5 Vision with ONNX Runtime on Azure Cobalt 100 on Arm
-
-
-
-draft: true
-cascade:
-    draft: true
+title: Deploy Phi-3.5 Vision with ONNX Runtime on Azure Cobalt 100 
 
 minutes_to_complete: 30
 
@@ -16,7 +10,7 @@ learning_objectives:
     - Analyze performance on Arm Neoverse-N2 based Azure Cobalt 100 VMs.
 
 prerequisites:
-    - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate cloud service provider. This Learning Path has been tested on a Microsoft Azure Cobalt 100 virtual machine with 32 cores, 8GB of RAM, and 32GB of disk space.
+    - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from an appropriate cloud service provider. This Learning Path has been tested on an Azure Cobalt 100 virtual machine.
     - Basic understanding of Python and machine learning concepts.
     - Familiarity with ONNX Runtime and Azure cloud services.
     - Knowledge of Large Language Model (LLM) fundamentals.

content/learning-paths/servers-and-cloud-computing/onnx/analysis.md

@@ -7,19 +7,22 @@ layout: learningpathall
 
 ## Try a text-only prompt
 
-To begin, skip the image prompt and input the text prompt as shown in the example below:
+To begin, skip the image prompt by pressing return and then input the text prompt as shown in the example below:
+
 ![output](output.png)
 
 Now exit the server.
 
 Next, download a sample image from the internet using the following `wget` command:
+
 ```bash
 wget https://cdn.pixabay.com/photo/2020/06/30/22/34/dog-5357794__340.jpg
 ```
 
 ## Try an image + text prompt
 
-After downloading the image, provide the image file name when prompted, followed by the text prompt, as demonstrated in the example below:
+After downloading the image, run the server again and provide the image file name when prompted, followed by the text prompt, as demonstrated in the example below:
+
 ![image_output](image_output.png)
 
 ## Observe performance metrics

content/learning-paths/servers-and-cloud-computing/onnx/chatbot.md

@@ -7,7 +7,7 @@ layout: learningpathall
 
 ## Create the chatbot server script
 
-Create a Python script called `phi3v.py` with the following content. 
+Create a Python script called `phi3v.py` with the code below.
 
 This script launches a chatbot server using the Phi-3.5 vision model and ONNX Runtime.
 

content/learning-paths/servers-and-cloud-computing/onnx/setup.md

@@ -13,12 +13,10 @@ In this Learning Path, you'll run quantized Phi models with ONNX Runtime on Micr
 
 Specifically, you'll deploy the Phi-3.5 vision model on Arm-based servers running Ubuntu 24.04 LTS. 
 
-
 {{% notice Note %}}
-These instructions have been tested on a 32-core Azure `Dpls_v6` instance.
+These instructions have been tested on a 32-core Azure `Dpls_v6` instance with 32 cores, 64GB of RAM, and 32GB of disk space.
 {{% /notice %}}
 
-
 You will learn how to build and configure ONNX Runtime to enable efficient LLM inference on Arm CPUs.
 
 This Learning Path walks you through the following tasks:
@@ -79,7 +77,7 @@ Clone and build the `onnxruntime-genai` repository, which includes the Kleidi AI
 Ensure you're using Python 3.12 to match the cp312 wheel format.
 {{% /notice %}}
 
-This build includes optimizations from Kleidi AI for efficient inference on Arm CPUs.
+This build includes optimizations from KleidiAI for efficient inference on Arm CPUs.
 
 ## Download and quantize the model