LLM
Running and Deploying LLMs using Red Hat OpenShift AI on ROSA cluster and Storing the Model in Amazon S3 Bucket
1. Introduction
Large Language Models (LLMs) are a specific type of generative AI focused on processing and generating human language. They can understand, generate, and manipulate human language in response to various tasks and prompts.
This guide is a simple example on how to run and deploy LLMs on a Red Hat OpenShift Services on AWS (ROSA) cluster, which is our managed service OpenShift platform on AWS, using Red Hat OpenShift AI (RHOAI) , which is formerly called Red Hat OpenShift Data Science (RHODS) and is our OpenShift platform for managing the entire lifecycle of AI/ML projects. And we will utilize Amazon S3 bucket to store the model output. In essence, here we will first install RHOAI operator and Jupyter notebook, create the S3 bucket, and then run the model.
Running and Deploying LLMs using Red Hat OpenShift AI on ROSA cluster and Storing the Model in Amazon S3 Bucket
1. Introduction
Large Language Models (LLMs) are a specific type of generative AI focused on processing and generating human language. They can understand, generate, and manipulate human language in response to various tasks and prompts.
This guide is a simple example on how to run and deploy LLMs on a Red Hat OpenShift Services on AWS (ROSA) cluster, which is our managed service OpenShift platform on AWS, using Red Hat OpenShift AI (RHOAI) , which is formerly called Red Hat OpenShift Data Science (RHODS) and is our OpenShift platform for managing the entire lifecycle of AI/ML projects. And we will utilize Amazon S3 bucket to store the model output. In essence, here we will first install RHOAI operator and Jupyter notebook, create the S3 bucket, and then run the model.
