Course Overview
Get an overview of the course, its intended audience, and the learning outcomes.
We'll cover the following
Educative welcomes you to the beginning of your journey to learn fine-tuning LLMs using LoRA!
Why take this course?
Large language models (LLMs) are deep learning models trained on a large corpus of data, including books, articles, websites, code repositories, and much more. They learn to understand the structure, pattern, and context of words in sentences and use this information to generate entirely new content, which includes text, images, audio, code, and more.
LLMs are capable of performing a variety of natural language processing (NLP) tasks, such as text generation, text classification, language translation, and much more. Even though the LLMs are trained on large datasets, they still lack specialization in specific areas. Fine-tuning addresses this limitation by allowing the model to be trained on a custom dataset, making it more accurate and effective for specific tasks and applications.
This course is designed to explore the fine-tuning concept and discuss different fine-tuning techniques, focusing on Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA).
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.
Target audience
This course is designed for a wide range of learners, including:
Data scientists: Data scientists looking to boost their productivity by expanding their knowledge and skills in fine-tuning techniques.
Software developers: Software developers who are interested in integrating fine-tuned models into their applications.
NLP practitioners: Professionals working in natural language processing, machine learning, or deep learning who want to improve their skills in fine-tuning large language models.
Students: Students in the field of computer science, AI, or NLP who want to learn about fine-tuning techniques and their applications.
Course structure
In this course, you’ll learn how to adapt pretrained LLMs to your specific needs, improving their accuracy and efficiency. Let’s briefly look at the course structure before diving into the details.
Basics of Fine-Tuning: Begin with the fundamentals of fine-tuning. Explore different types of fine-tuning along with their use cases, pros, and cons. By the end, you’ll explore the concept of quantization and will be able to apply it to any model for fine-tuning.
Exploring LoRA: Dive into the most widely used techniques of parametric efficient fine-tuning (PEFT), i.e., LoRA and QloRA, and how they work under the hood. Get hands-on experience in fine-tuning the Llama 3 model on a custom dataset.
Wrap Up: To conclude the course, solve a case study to revise the concepts and discuss emerging trends in AI.
By the end of this course, you’ll be able to fine-tune LLMs to achieve state-of-the-art performance in your specific applications.
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.