Moving To Substack

2025-03-25 · Source: Jay Alammar · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The author is migrating their blog content to Substack, citing a more convenient authoring experience, effective March 26, 2025. Readers are encouraged to follow the new Substack and review "The Illustrated DeepSeek R-1." Additionally, the post promotes a course titled "How Transformer LLMs Work," developed with Jay Alammar and Martin Görner, authors of "Hands-On Language Models." This course provides a deep technical understanding of Transformer network architecture, which underpins modern generative AI models like GPT. It covers concepts such as attention mechanisms, KV cache, tokenization, contextual embeddings, and the evolution of the Transformer block, with practical examples using the Hugging Face Transformers library.

Key takeaway

For AI Engineers and Machine Learning Engineers seeking to deepen their understanding of foundational generative AI, enrolling in the "How Transformer LLMs Work" course is highly recommended. You will gain critical intuition into Transformer architecture, attention mechanisms, and tokenization, which are essential for building and optimizing applications with large language models.

Key insights

The Transformer architecture is fundamental to modern generative AI, enabling advanced language model capabilities.

Principles

• Visualizations simplify complex technical concepts.
• Contextual embeddings capture word meaning.
• Tokenization breaks text for LLM processing.

Method

The course teaches Transformer LLM mechanics by explaining attention, KV cache, tokenization, embeddings, and decoder-only generation, using code examples and Hugging Face Transformers.

In practice

• Explore Hugging Face Transformers library.
• Understand tokenizers for LLM inputs.
• Study contextual embeddings for meaning.

Topics

• Transformer Architecture
• Large Language Models
• Attention Mechanisms
• Generative AI
• Hugging Face Transformers

Best for: AI Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• How Transformers Architecture Powers Modern LLMs — LLMs use a transformer architecture to convert text into numerical representations, process context, and predict the next token.
• My Deep Dive into Large Language Models: An Architectural Journey — The shift to LLMs redefines NLP through scalable Transformer architectures, enabling versatile language tasks and requiring advanced customization.
• The Ultimate Transformer Course for Working Engineers — The course offers practical understanding of transformers to debug LLM issues like slow inference and OOM errors.
• Building LLM From Scratch: Understanding How Large Language Models Work — Large Language Models fundamentally predict the next token by processing numerical representations of text through attention mechanisms.
• What Is an LLM, and What Are Transformer Models? A 2026 Field Guide for Leaders and Builders — Large Language Models are next-token prediction engines powered by the Transformer architecture, balancing power with occasional errors.
• Building a Small Language Model (1) — Understanding Transformer — LLMs predict the next token by processing numerical representations of text through a multi-stage Transformer architecture.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Jay Alammar.