This project provides concise, easily digestible technical tutorials on cutting-edge concepts, primarily focusing on Large Language Models (LLMs) and related AI technologies. It targets engineers, researchers, and power users seeking to quickly understand complex topics, with each article designed for a ~5-minute read.

How It Works

The project curates and explains advanced technical concepts through short, focused articles. It covers a wide range of LLM-related topics, including model formats (GGUF), inference techniques (speculative decoding), optimization strategies (Flash Attention, LoRA), and deployment considerations (ONNX, Safetensors). The emphasis is on clarity and brevity, making complex subjects accessible.

Quick Start & Requirements

No installation or specific requirements are needed to read the content, as it is presented as a collection of articles.

Highlighted Details

• Extensive coverage of LLM internals, including attention mechanisms (Multi-Head, Multi-Query, Grouped Query), quantization, distillation, and RAG.
• Explanations of model formats like GGUF and deployment tools like ONNX and Safetensors.
• Tutorials on practical aspects such as local LLM deployment and fine-tuning best practices.
• Inclusion of foundational AI and computer science concepts like vector embeddings, AI agents, and Transformer architecture.

Maintenance & Community

The project is maintained by @karminski and welcomes contributions via issues or pull requests.

Licensing & Compatibility

This project is licensed under the MIT License, which permits commercial use and integration into closed-source projects.

Limitations & Caveats

The author acknowledges potential inaccuracies due to personal limitations and encourages community feedback for corrections and improvements.