Free Mastery Course: Become a Master of Language Models

In this blog post, we will review a famous educational GitHub repository with 24K stars. This repository provides a framework to help you master large language models (LLM) for free. We will discuss the course structure, Jupyter notebooks containing code examples, and articles covering the latest LLM developments.

He Large language model course is a comprehensive program designed to equip students with the skills and knowledge necessary to excel in the rapidly evolving field of major language models. It consists of three main parts that cover fundamental and advanced tools and concepts. Each main section contains various topics that come with tutorials, guides, and YouTube resources that are freely available online.

The LLM course is a useful guide that provides a structured way of learning by providing freely available resources, tutorials, videos, workbooks and articles in one place. Even if you are a complete beginner, you can start with the fundamentals section and learn about technical and various algorithms and tools to solve simple natural language and machine learning problems.

The course is divided into three main parts, each focusing on a different aspect of the LLM experience:

LLM Fundamentals

This essential part addresses the essential knowledge required to understand and work with LLM. Covers mathematics, Python programming, the basics of neural networks, and natural language processing. For anyone looking to delve into machine learning or deepen their understanding of its mathematical foundations, this section is invaluable. The resources provided, from 3Blue1Brown's engaging video series to Khan Academy's comprehensive courses, offer a variety of learning paths suitable for different learning styles.

Topics covered:

1. Mathematics for machine learning
2. Python for machine learning
3. Neural networks
4. Natural Language Processing (NLP)

The LLM Scientist

This LLM Scientist guide is designed for people interested in developing cutting-edge LLMs. It covers the architecture of LLMs, including Transformer and GPT models, and delves into advanced topics such as quantization, attention mechanisms, fine-tuning, and RLHF. The guide explains each topic in detail and provides tutorials and various resources to solidify the concepts. The whole concept is learning by building.

Topics covered:

1. The architecture of the LLM
2. Building an instruction data set
3. Pre-training models
4. Supervised adjustment
5. Reinforcement learning from human feedback
6. Assessment
7. Quantization
8. New tendencies

The LLM Engineer

This part of the course focuses on the practical application of the LLM. You will guide students through the process of creating LLM-based applications and implementing them. Topics covered include running LLM, creating vector databases for augmented retrieval generation, advanced RAG techniques, inference optimization, and deployment strategies. During this part of the course, you will learn about the LangChain and Pinecone framework for vector databases, which are essential for integrating and implementing LLM solutions.

Topics covered:

1. LLM Execution
2. Building vector storage
3. Recovery Generation Increased
4. advanced RAG
5. Inference optimization
6. LLM Implementation
7. Secure LLM

Creating, tuning, inferring, and deploying models can be quite complex, requiring knowledge of several tools and careful attention to GPU and RAM memory usage. This is where the course offers a comprehensive collection of notebooks and articles that can serve as useful references for implementing the concepts discussed.

Notebooks and Articles about:

• Tools: Covers tools to automatically evaluate your LLMs, merge models, quantify LLMs in GGUF format, and visualize fusion models.
• Fine tuning: Provides a Google Colab notebook for step-by-step guides on how to tune models like Llama 2 and use advanced techniques to improve performance.
• Quantization: The quantization notebooks delve into optimizing LLMs for efficiency using 4-bit GPTQ and GGUF quantization methodologies.

Whether you are a beginner looking to understand the basics or a seasoned professional looking to stay up to date with the latest research and applications, the master's degree course in Law is an excellent resource to delve deeper into the world of LLMs. Provides a wide range of freely available resources, tutorials, videos, workbooks and articles, all in one place. The course covers all aspects of LLMs, from theoretical foundations to cutting-edge LLM implementation, making it a must-have course for anyone interested in becoming an LLM expert. In addition, notebooks and articles are included to reinforce the concepts covered in each section.

Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master's degree in technology Management and a Bachelor's degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.