Course Overview

Get an overview of the course, its intended audience, and the learning outcomes.

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We’re excited to have you here as you embark on your journey to master fine-tuning LLMs with advanced techniques like LoRA and QLoRA. This course will give you the theoretical understanding and practical skills to customize large language models like Llama 3, optimize them for specific tasks, and deploy them efficiently.

Why take this course?

Large language models (LLMs) are at the forefront of machine learning innovation, capable of performing diverse tasks like:

  • Text generation (e.g., drafting essays or writing code)

  • Text classification (e.g., sentiment analysis or spam detection)

  • Language translation (e.g., translating content across languages)

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Despite their versatility, pretrained models often lack specialization for specific applications. This is where fine-tuning steps in, allowing you to adapt these powerful models to your unique needs.

In this course, you’ll learn how to:

  • Use Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) to efficiently fine-tune massive models with minimal resource requirements.

  • Apply quantization techniques like int8 and bits and bytes quantization to reduce the model size and enhance deployment efficiency.

  • Customize and optimize Llama 3 to achieve state-of-the-art performance for your tasks.

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Fine-tuning LLMs
Fine-tuning LLMs

Who is this course for?

This course is designed for a wide range of learners, including:

  • Data scientists seeking to adapt LLMs for specialized data.

  • Software developers aiming to integrate fine-tuned models into applications.

  • NLP practitioners looking to refine their skills in parameter-efficient fine-tuning (PEFT).

  • Students interested in practical applications of advanced AI techniques.

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What will you learn?

Here’s a brief overview of the course structure:

  • Basics of fine-tuning:

    • Understand what fine-tuning is and explore its key techniques.

    • Learn the quantization process and how it helps reduce model size while maintaining accuracy.

  • Exploring LoRA and QLoRA:

    • Dive deep into LoRA fine-tuning and Quantized Low-Rank Adaptation (QLoRA) for resource-efficient model customization.

    • Gain hands-on experience fine-tuning the Llama 3 model with custom datasets.

  • Practical applications and wrap up:

    • Apply your knowledge in a capstone project, solving a real-world problem with LLM fine-tuning.

    • Explore emerging trends in AI and fine-tuning techniques.

Course structure

Prerequisites

Following are some prerequisites that can help you get the most out of this course:

  • Programming language: Proficiency in Python and libraries like PyTorch, Hugging Face’s Transformers, etc.

  • NLP concepts: Familiarity with the fundamental concepts of artificial intelligence, such as machine learning, generative AI, and deep learning frameworks like PyTorch or TensorFlow.

  • Large language models: Understanding of large language models (LLMs) and transformer architecture.

  • Basic NLP principles: Familiarity with concepts like tokenization, stemming, lemmatization, and embeddings.

Course strengths

The following are some key strengths of the course. We have summarized the strengths and the advantages of this course in the table given below:

Strength

Advantages

Structured learning

We've logically structured our course by starting from the basics and gradually moving to more complex topics, ensuring a smooth learning curve for learners.

Comprehensice fine-tuning coverage


We have provided a comprehensive understanding of various fine-tuning techniques, enabling learners to effectively adapt and apply them.


In-depth LoRA and QLoRA exploration


We have covered an in-depth explanation of LoRA and QLoRA architectures, providing learners with a thorough understanding of their applications.

Hands-on experience

We provide hands-on learning to learners by providing GPU-powered execution of codes within our course, reinforcing theoretical knowledge through application.

Along the way, we’ll leave little notes called “Educative Bytes,” which will contain interesting facts or answers to questions that might arise in your mind while reading the material. These bytes are designed to enhance your learning experience and provide deeper insights into the fascinating world of fine-tuning LLMs.