This repository contains an incomplete e-book, "llm_illustrated," aimed at explaining large language model (LLM) technologies through visual aids and simplified code examples. It targets individuals seeking an accessible, graphical introduction to LLM concepts, offering a visual learning experience for complex topics.

How It Works

The project focuses on illustrating core LLM components like self-attention mechanisms, positional encodings, and KV caching using diagrams and code snippets. The approach prioritizes visual clarity and conceptual understanding over exhaustive technical detail, making it easier for beginners to grasp the underlying principles of LLM architecture.

Quick Start & Requirements

• Access: Follow the project's WeChat official account and reply "看图学大模型" for the latest PDF version.
• Prerequisites: None explicitly stated for viewing the PDF. Development or contribution would require LaTeX knowledge.
• Resources: PDF viewing requires a standard reader.

Highlighted Details

• Visual explanations of self-attention structure and code.
• Diagrams illustrating KV Cache and absolute positional encoding.
• Visualizations of Transformer components and Dartmouth Conference participant relationships.
• Content is presented in a graphical, easy-to-understand format.

Maintenance & Community

• The project is in early development (<10% complete).
• Updates are provided via a WeChat official account.

Licensing & Compatibility

• No license is specified in the provided README.

Limitations & Caveats

The e-book is significantly incomplete. PDF formatting and LaTeX typesetting require further adjustments, indicating potential layout issues in the current version.