This repository serves as a comprehensive, beginner-friendly knowledge base for understanding Large Language Models (LLMs). It targets individuals preparing for LLM interviews or seeking to grasp core concepts, offering explanations on pre-training, deployment, fine-tuning, quantization, and more, aiming to empower users to discuss LLMs confidently.

How It Works

The project is structured as a curated collection of articles and explanations covering various facets of LLMs. It breaks down complex topics like Transformer architectures, optimizers (SGD, AdamW), activation functions (SwiGLU, GELU), attention mechanisms (FlashAttention, RoPE), tokenization, and parallel training strategies. The content is presented in a digestible, article-based format, often with a focus on intuitive understanding and practical application.

Quick Start & Requirements

This repository is a knowledge base, not a runnable software project. No installation or specific requirements are needed beyond a web browser to read the content.

• Official Docs: https://github.com/luhengshiwo/LLMForEverybody

Highlighted Details

• Extensive coverage of LLM training concepts, including detailed breakdowns of optimizers, activation functions, and attention mechanisms.
• Practical guides on LLM deployment and inference, discussing frameworks like vLLM and TensorRT-LLM.
• In-depth exploration of fine-tuning techniques such as LoRA, QLoRA, and prompt tuning.
• Explanations of essential mathematical foundations for LLMs, including linear algebra, calculus, and probability statistics.
• Discussions on enterprise adoption challenges, prompt engineering, Agents, and RAG.

Maintenance & Community

The project appears to be a personal knowledge compilation. There are no explicit mentions of contributors, sponsorships, or community channels like Discord/Slack.

Licensing & Compatibility

The repository does not specify a license.

Limitations & Caveats

As a curated collection of articles, the project does not offer runnable code or direct LLM functionality. The content's depth and accuracy rely on the author's compilation and may not represent the absolute latest advancements or offer alternative perspectives.