This repository provides a curated study guide for learning about Transformer models, targeting students and practitioners in machine learning and NLP. It offers a structured path from high-level introductions to in-depth technical explanations and practical implementations, aiming to accelerate understanding and application of this crucial architecture.

How It Works

The guide follows a pedagogical approach, starting with accessible, high-level introductions and illustrated explanations from prominent sources like Jay Alammar. It then progresses to more technical summaries and detailed breakdowns of Transformer components, referencing Lilian Weng's blog posts. The core learning loop emphasizes understanding theory before diving into implementation, with a focus on "The Annotated Transformer" for hands-on experience.

Quick Start & Requirements

This repository is a curated list of resources, not a runnable codebase. No installation or specific requirements are needed to access the study materials.

• Resources:
  • Introduction to Transformer - Lecture Notes
  • The Illustrated Transformer
  • The Annotated Transformer
  • Transformers library by HuggingFace

Highlighted Details

• Comprehensive coverage from introductory concepts to detailed technical breakdowns.
• Emphasis on practical implementation through "The Annotated Transformer."
• Links to seminal papers like "Attention Is All You Need."
• Includes resources for applying Transformers via HuggingFace and further reading on LLMs.

Maintenance & Community

The repository is maintained by dair-ai and welcomes suggestions for study materials. Updates are planned to include more applications, papers, and code implementations. Follow on Twitter for updates.

Licensing & Compatibility

The repository itself contains links to external resources, each with its own licensing. The primary focus is on educational content and pointers to libraries like HuggingFace Transformers, which has its own Apache 2.0 license.

Limitations & Caveats

This is a study guide and does not provide a unified codebase or interactive environment. Users will need to independently follow the provided links to access and utilize the learning materials and implementation resources.