Rag Tuning Llm Models

The Individual Processes Involved With RAG Tuning LLM Models and How To Deploy Them

What you'll learn

What is RAG and why is it useful for LLMs?

What are the benefits and challenges of RAG tuning?

How to fine-tune a RAG model on a specific task or domain?

How to optimize the RAG model for speed and memory efficiency?

Requirements

Python programming PyTorch framework Natural language processing Large language models Hugging Face Transformers library

Description

By the end of this course, you will have a solid understanding of RAG and how to use it for various natural language processing tasks and applications. You will also have a portfolio of RAG projects that you can showcase to potential employers or clients.This course is designed for anyone who is interested in natural language processing and large language models, and who wants to learn how to use RAG for retrieving and generating natural language. To follow this course, you will need some basic knowledge and skills in the following areas:Python programmingPyTorch frameworkNatural language processingLarge language modelsHugging Face Transformers libraryIf you are not familiar with any of these topics, don’t worry, we will provide some resources and references for you to learn more about them. However, we recommend that you have some prior experience and interest in natural language processing and large language models, as this will help you get the most out of this course.This course is divided into six sections, each covering a different aspect of RAG. The first section is the introduction, where you will learn what is RAG and why it is useful for LLMs. The second section is the RAG framework, where you will learn how RAG works and what are its components. The third section is the RAG tuning, where you will learn how to fine-tune, evaluate, and optimize RAG models. The fourth section is the RAG applications, where you will learn how to build and deploy RAG-based LLM applications from scratch. The fifth section is the RAG optimization, where you will learn how to optimize RAG models for speed and memory efficiency. The sixth and final section is the conclusion, where you will learn about the current limitations and future directions of RAG research.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 The RAG Framework and Its Components

Lecture 3 Fine Tuning A RAG Model On A Specific Task Or Domain

Lecture 4 How To Optimize the RAG Model For Speed and Memory Efficiency

Lecture 5 Building A RAG-Based LLM Application From Scratch and Deploying It In Production

Lecture 6 Current Limitations and Future Directions of RAG Research

Lecture 7 A Field Trip To Real World RAG-Based Applications

Anyone who is interested in RAG Tuning LLM Models