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RAG Tuning LLM Models
Rating: 3.3 out of 5(20 ratings)
201 students

RAG Tuning LLM Models

The Individual Processes Involved With RAG Tuning LLM Models and How To Deploy Them
Created byRichard Aragon
Last updated 6/2024
English

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?

Course content

1 section • 16 lectures • 3h 7m total length

  • Introduction6:55
  • The RAG Framework and Its Components9:08
  • Fine Tuning A RAG Model On A Specific Task Or Domain5:02
  • How To Optimize the RAG Model For Speed and Memory Efficiency6:37
  • Building A RAG-Based LLM Application From Scratch and Deploying It In Production5:43
  • Current Limitations and Future Directions of RAG Research8:26
  • A Field Trip To Real World RAG-Based Applications9:33
  • January 2024 Update To Fine Tuning Methods41:28
  • ChromaDB Open Source Database Vectorization For RAG Models17:27
  • Langroid Agent RAG Tuning Lecture12:34

    Learn Langroid's multi-agent framework for rag tuning LLM models, enabling coordinated agents to perform function calls, retrieval augmented generation, and interact with databases through prompts.

  • ROPE Fine Tuning10:04
  • Self Rewarding LLMs Fine Tuning Method13:58
  • RAFT7:32
  • RAG Tuning vs Fine Tuning16:30

    Rag tuning relies on external retrieval during inference and doesn't update embeddings, unlike fine tuning which internalizes knowledge. This leads to higher costs and privacy concerns.

  • MoRa Fine Tuning7:31
  • GraphReader8:59

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 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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