Typo and linting (#945)

Author: andytael

docs-source/spring/content/infrastructure/gpu/_index.md

@@ -4,14 +4,9 @@ description: "GPUs for AI workload"
 keywords: "gpu ollama llm spring springboot microservices oracle"
 ---
 
-Oracle Backend for Spring Boot and Microservices provides an option during installation to provision a set
-of Kubernetes nodes with NVIDIA A10 GPUs that are suitable for running AI workloads.  If you choose that option during
-installation, you may also specify how many nodes are provisioned.  The GPU nodes will be in a separate
-Node Pool to the normal CPU nodes, which allows you to scale it independently of the CPU nodes.
-They are also labeled so that you can target appropriate workloads to them using node selectors
-and/or affinity rules.
+Oracle Backend for Spring Boot and Microservices provides an option during installation to provision a set of Kubernetes nodes with NVIDIA A10 GPUs that are suitable for running AI workloads.  If you choose that option during installation, you may also specify how many nodes are provisioned.  The GPU nodes will be in a separate Node Pool to the normal CPU nodes, which allows you to scale it independently of the CPU nodes. They are also labeled so that you can target appropriate workloads to them using node selectors and/or affinity rules.
 
-To view a list of nodes in your cluster with a GPU, you can use this command: 
+To view a list of nodes in your cluster with a GPU, you can use this command:
 
 ```bash
 $ kubectl get nodes -l 'node.kubernetes.io/instance-type=VM.GPU.A10.1'
@@ -21,12 +16,9 @@ NAME           STATUS   ROLES   AGE     VERSION
 
 ## Running a Large Language Model on your GPU nodes
 
-One very common use for GPU nodes is to run a self-hosted Large Language Model (LLM)
-such as `llama3` for inferencing or `nomic-embed-text` for embedding.
+One very common use for GPU nodes is to run a self-hosted Large Language Model (LLM) such as `llama3` for inferencing or `nomic-embed-text` for embedding.
 
-Companies often want to self-host an LLM to avoid sending private or sensitive data
-outside of their organization to a third-party provider, or to have more control over
-the costs of running the LLM and assocatied infrastructure.
+Companies often want to self-host an LLM to avoid sending private or sensitive data outside of their organization to a third-party provider, or to have more control over the costs of running the LLM and associated infrastructure.
 
 One excellent way to self-host LLMs is to use [Ollama](https://ollama.com/).
 
@@ -44,9 +36,7 @@ To install Ollama on your GPU nodes, you can use the following commands:
     helm repo update
     ```
 
-1. Create a `values.yaml` file to configure how Ollama should be installed, including
-   which node(s) to run it on.  Here is an example that will run Ollama on a GPU node
-   and will pull the `llama3` model.
+1. Create a `values.yaml` file to configure how Ollama should be installed, including which node(s) to run it on.  Here is an example that will run Ollama on a GPU node and will pull the `llama3` model.
 
     ```yaml
     ollama:
@@ -60,8 +50,7 @@ To install Ollama on your GPU nodes, you can use the following commands:
       node.kubernetes.io/instance-type: VM.GPU.A10.1
     ```
 
-  For more information on how to configure Ollama using the helm chart, refer to
-  [its documentation](https://artifacthub.io/packages/helm/ollama-helm/ollama).
+  For more information on how to configure Ollama using the helm chart, refer to [its documentation](https://artifacthub.io/packages/helm/ollama-helm/ollama).
 
 1. Create a namespace to deploy Ollama in:
 
@@ -77,8 +66,7 @@ To install Ollama on your GPU nodes, you can use the following commands:
 
 ### Interacting with Ollama
 
-You can interact with Ollama using the provided command line tool, called `ollama`.
-For example, to list the available models, use the `ollama ls` command:
+You can interact with Ollama using the provided command line tool, called `ollama`. For example, to list the available models, use the `ollama ls` command:
 
 ```bash
 kubectl -n ollama exec svc/ollama -- ollama ls
@@ -88,17 +76,13 @@ llama3:latest   365c0bd3c000    4.7 GB  2 minutes ago
 
 To ask the LLM a question, you can use the `ollama run` command:
 
-```
+```bash
 $ kubectl -n ollama exec svc/ollama -- ollama run llama3 "what is spring boot?"
-Spring Boot is an open-source Java-based framework that simplifies the development
-of web applications and microservices. It's a subset of the larger Spring ecosystem,
-which provides a comprehensive platform for building enterprise-level applications.
+Spring Boot is an open-source Java-based framework that simplifies the development of web applications and microservices. It's a subset of the larger Spring ecosystem, which provides a comprehensive platform for building enterprise-level applications.
 
 ...
 ```
 
 ### Using LLMs hosted by Ollama in your Spring application
 
-Our self-paced hands-on example **CloudBank AI** includes an example of how
-to [build a simple chatbot](https://oracle.github.io/microservices-datadriven/cloudbank/springai/simple-chat)
-using Spring AI and Ollama.
+Our self-paced hands-on example **CloudBank AI** includes an example of how to [build a simple chatbot](https://oracle.github.io/microservices-datadriven/cloudbank/springai/simple-chat) using Spring AI and Ollama.