RAG Tuning LLM Models | Udemy

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?

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 programming

PyTorch framework

Natural language processing

Large language models

Hugging Face Transformers library

If 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.

Who this course is for:
Anyone who is interested in RAG Tuning LLM Models

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