Jupyter
Creating RAG Chatbot using TinyLlama and LangChain with Red Hat OpenShift AI on ARO
1. Introduction
Retrieval-Augmented Generation (RAG) is a technique to enhance Large Language Models (LLMs) to retrieve relevant information from a knowledge base before generating responses, rather than relying solely on their training. LangChain is a framework for developing applications powered by language models. It provides tools and APIs that make it easier to create complex applications using LLMs, such as using RAG technique to enable the chatbot to answer questions based on the provided document.
Creating Images using Stable Diffusion on Red Hat OpenShift AI on ROSA cluster with GPU enabled
1. Introduction
Stable Diffusion is an AI model to generate images from text description. It uses a diffusion process to iteratively denoise random Gaussian noise into coherent images. This is a simple tutorial to create images using Stable Diffusion model using Red Hat OpenShift AI (RHOAI) , formerly called Red Hat OpenShift Data Science (RHODS), which is our OpenShift platform for AI/ML projects lifecycle management, running on a Red Hat OpenShift Services on AWS (ROSA) cluster, which is our managed service OpenShift platform on AWS, with NVIDIA GPU enabled.
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.
