RHEL AI POC

This repository contains the code and documentation for creating RHEL AI Proofs of Concept (POCs). This aims to be straightforward and comply with the available documentation for RHEL AI and InstructLab. The goal is to provide a simple and easy to follow guide for creating successful and reproducible POCs that can be used to demonstrate the capabilities of RHEL AI.

Much of what is here, are some nuance and gotchas that may not be covered in the main documentation. There are some included scripts and automation, but many of these are anticipated to be removed as the features are integrated into the main RHEL AI product.

Table of Contents

• RHEL AI POC
  • Table of Contents
  • Suggested documentation
  • Prerequisites
  • Installation and Configuration
    • Bare Metal
    • IBM Cloud
    • AWS
      • Creating an AWS AMI
      • Launching an AWS Instance
  • Document Collection
    • PDF
    • qna.yaml
    • Evaluation Questions
  • Data Preparation
    • PDF to Markdown Conversion
  • Synthetic Data Generation
    • ilab generate
  • Model Training
    • ilab train
  • Deployment and Testing
    • Saving the model to S3 storage
    • Serving the model
      • ilab serve
      • OpenShift AI Serving
  • Evaluation
    • Testing the model
    • RAG with Anything LLM
    • RAG testing with InstructLab

Suggested documentation

• RHEL AI
• InstructLab

Prerequisites

Installation and Configuration

Bare Metal

TBD

IBM Cloud

TBD

AWS

Creating an AWS AMI

Launching an AWS Instance

Document Collection

PDF

qna.yaml

Evaluation Questions

Data Preparation

PDF to Markdown Conversion

Synthetic Data Generation

ilab generate

Model Training

ilab train

Deployment and Testing

Saving the model to S3 storage

Serving the model

ilab serve

OpenShift AI Serving

Evaluation

Testing the model

RAG with Anything LLM

RAG testing with InstructLab