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Improving the Performance of Your LLM Beyond Fine Tuning
Rating: 4.2 out of 5(28 ratings)
686 students

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!
Created byRichard Aragon
Last updated 10/2024
English

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

Course content

14 sections • 34 lectures • 5h 2m total length

  • Introduction7:36

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 model

  • Section 2: How to use domain adaptation techniques to reduce the mismatch and inconsistency of your data for fine tuning your LLM model

  • Section 3: How to use model pruning techniques to reduce the complexity and size of your LLM model after fine tuning it

  • Section 4: How to use model distillation techniques to improve the efficiency and speed of your LLM model after fine tuning it

By 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 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

This 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.

Who this course is for:

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

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