Improving The Performance Of Your Llm Beyond Fine Tuning

Everything A Business Needs To Fine Tune An LLM Model On Their Own Data, And Beyond!

What you'll learn

Explain the importance and benefits of improving the performance of your LLM model beyond traditional fine tuning methods

Identify and apply the data augmentation techniques that can increase the quantity and diversity of your data for fine tuning your LLM model

Identify and apply the domain adaptation techniques that can reduce the mismatch and inconsistency of your data for fine tuning your LLM model

Identify and apply the model pruning techniques that can reduce the complexity and size of your LLM model after fine tuning it

Identify and apply the model distillation techniques that can improve the efficiency and speed of your LLM model after fine tuning it

Requirements

Python and PyTorch experience are highly recommended for this course.

Description

In this course, we will explore some techniques and methods that can help you improve the performance of your LLM model beyond traditional fine tuning methods. You should purchase this course if you are a business leader or a developer who is interested in fine tuning your LLM model. These techniques and methods can help you overcome some of the limitations and challenges of fine tuning by enhancing the quality and quantity of your data, reducing the mismatch and inconsistency of your data, reducing the complexity and size of your LLM model, and improving the efficiency and speed of your LLM model.The main topics that we will cover in this course are:Section 1: How to use data augmentation techniques to increase the quantity and diversity of your data for fine tuning your LLM modelSection 2: How to use domain adaptation techniques to reduce the mismatch and inconsistency of your data for fine tuning your LLM modelSection 3: How to use model pruning techniques to reduce the complexity and size of your LLM model after fine tuning itSection 4: How to use model distillation techniques to improve the efficiency and speed of your LLM model after fine tuning itBy the end of this course, you will be able to:Explain the importance and benefits of improving the performance of your LLM model beyond traditional fine tuning methodsIdentify and apply the data augmentation techniques that can increase the quantity and diversity of your data for fine tuning your LLM modelIdentify and apply the domain adaptation techniques that can reduce the mismatch and inconsistency of your data for fine tuning your LLM modelIdentify and apply the model pruning techniques that can reduce the complexity and size of your LLM model after fine tuning itIdentify and apply the model distillation techniques that can improve the efficiency and speed of your LLM model after fine tuning itThis course is designed for anyone who is interested in learning how to improve the performance of their LLM models beyond traditional fine tuning methods. You should have some basic knowledge of natural language processing, deep learning, and Python programming. I hope you are excited to join me in this course.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Data Augmentation Techniques to Increase the Quantity and Diversity of Your Data

Lecture 2 Section 1 Lecture 1

Lecture 3 Section 1 Lecture 2

Section 3: How to use domain adaptation techniques to reduce mismatch and inconsistency

Lecture 4 Section 2 Lecture 1

Lecture 5 Section 2 Lecture 2

Lecture 6 Section 2 Lecture 3

Section 4: Model Pruning Techniques

Lecture 7 Section 3 Lecture 1

Lecture 8 Section 3 Lecture 2

Lecture 9 Section 3 Lecture 3

Lecture 10 Section 3 Lecture 4

Lecture 11 Section 3 Lecture 5

Section 5: Model Distillation Techniques

Lecture 12 Section 4 Lecture 1

Lecture 13 Section 4 Lecture 2

Lecture 14 Section 4 Lecture 3

Lecture 15 Section 4 Lecture 4

Lecture 16 Section 4 Lecture 5

This course is made with a very technical slant, you should have at least a base level knowledge of Python before attempting this course.