Update Blog “deploying-a-hugging-face-llm-in-hpe-private-cloud-ai”

Author: ealagna

content/blog/deploying-a-hugging-face-llm-in-hpe-private-cloud-ai.md

@@ -11,19 +11,19 @@ tags:
   - vllm
   - SLM
 ---
-Deploying new language models for for users to interact with can be challenging for beginners. HPE has developed Private Cloud AI to help users get to productive AI work quickly and easily. 
+Deploying new language models for for users to interact with can be challenging for beginners. HPE developed Private Cloud AI to help users set up and implement AI solutions quickly and easily. 
 
-Here we will show how to use HPE Machine Learning Inference Service as a part of HPE Private Cloud AI to add a new packaged model from a Hugging Face repository and create an endpoint to query the model. This is done in a Jupyter Notebook.
+In this post, we will show how to use the HPE Machine Learning Inference Service (MLIS) as a part of HPE Private Cloud AI to add a new packaged model from a Hugging Face repository and create an endpoint to query the model. This is done using a Jupyter Notebook.
 
 ### Prerequisites
 
 This tutorial uses the [HPE Private Cloud AI](https://www.hpe.com/us/en/private-cloud-ai.html) (PCAI) platform. A PCAI system is required for these steps to work. It is assumed that the PCAI system is physically installed, patched and running with user accounts provisioned.
 
-### Steps to Deploy
+### Steps to deploy
 
-First we need to choose a model to deploy, in this case we've chosen a model hosted on Hugging Face called [SmolLM2 1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct). This is a compact model that can solve a wide range of problems even though it is relatively diminutive at 1.7B parameters.
+First, you will need to choose a model to deploy. In this case, we've chosen a model hosted on Hugging Face called [SmolLM2 1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct). This is a compact model that can solve a wide range of problems even though it is relatively diminutive at 1.7B parameters.
 
-### Launching the Interface
+### Launching the interface
 
 ![Computer screen showing the HPE Private Cloud AI user interface and the HPE MLIS tile is highlighted.](/img/mlis.png)
 
@@ -49,7 +49,7 @@ When the process is complete an endpoint will be provided.
 
 ![Endpoint provided by MLIS system](/img/endpoint.png)
 
-Next up let's take the now deployed model that's ready for inference and connect to it and interact with it from a Jupyter Notebook.
+Next up, let's take the now deployed model that's ready for inference and connect to it and interact with it from a Jupyter Notebook.
 
 ### Building the Jupyter Notebook
 
@@ -61,7 +61,7 @@ First install `openai` if you do not already have it and import.
 from openai import OpenAI
 ```
 
-Then using the endpoint and key genered by HPE MLIS enter them into your Jupyter Notebook. Be sure to append /v1 to the URL.
+Then using the endpoint and key generated by HPE MLIS enter them into your Jupyter Notebook. Be sure to append /v1 to the URL.
 
 ```python
 # Grab endpoint URL and API key from MLIS, remember to include "/v1" for latest version of the OpenAI-compatible API
@@ -70,7 +70,7 @@ openai_api_base = "https://smollm2-1-7b-vllm-predictor-dave-wright-hpe-1073f7cd.
 openai_api_key = "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJpYXQiOjE3Mzk5MzgzMzAsImlzcyI6ImFpb2xpQGhwZS5jb20iLCJzdWIiOiI5MjNhM2JhOC1mMGU4LTQxOTQtODNkMS05ZWY4NzNjZGYxOWYiLCJ1c2VyIjoiZGF2ZS53cmlnaHQtaHBlLmNvbSJ9.YwH9gGPxTWxy4RSdjnQA9-U3_u7P0OIcarqw25DV8bOiftU1L4IvvyERHspj2lMGtZWbff1F3uh84wjAePHaHDcDTLoGtq6gJYwo_qRU03xV8Q2lwBetCCLUE4OHqS608gjJ-j1SLyqwxFxlXkqMOtnBY5_nswlAwCzHV28P8u8XxxfWuXFmoJpSA1egCWVVfEoTuK8CTz9kUJJ5opSp6m8qdqJmC2qxH0igcpKmL2H_MZ-62UHfEf240VRtc0DRNlOjeCoDM79aVPs3SjCtGeVkeEHimJwJbfGFIcu3LibX3QjbABUzWb5BPPZjzyEYUVM5ak12_sJ8j1mUW-r0sA"
 ```
 
-Now we need an OpenAI client interface.
+Now, we need an OpenAI client interface.
 
 ```python
 # create OpenAI client interface
@@ -124,9 +124,9 @@ def chat():
 
 ![Jupyter Notebook showing imported model endpoint and API key.](/img/jupyter.png)
 
-Then interacting with the model is done through a simple chat.
+Then, interacting with the model is done through a simple chat.
 
-![Interaction with the SmolLM2 small language model in a Jupyter Notebook](/img/chat-interface.png)
+![Interaction with the SmolLM2 Small Language Model in a Jupyter Notebook](/img/chat-interface.png)
 
 For a recorded demonstration that shows the process real time see this screen recording. <https://www.youtube.com/watch?v=oqjc-2c1Vtk>