This repository provides over 100 original, visually detailed diagrams explaining Large Language Models (LLMs) and Reinforcement Learning (RL) concepts, aimed at engineers and researchers seeking a deep understanding of these fields. It serves as a visual companion to the book "Large Model Algorithms: Reinforcement Learning, Fine-Tuning, and Alignment," offering clear explanations of complex architectures and training methodologies.

How It Works

The project breaks down LLM and RL concepts into digestible visual components, covering everything from fundamental Transformer architectures and decoding strategies to advanced RL algorithms like PPO, DPO, and RLHF. Each diagram is designed to illustrate specific mechanisms, such as the flow of data through an LLM, the training loop of DPO, or the interplay of multiple models in RLHF, providing a comprehensive visual guide.

Quick Start & Requirements

• Access: Diagrams are available as high-resolution images within the repository or as scalable SVG files for infinite zoom and text selection.
• Resources: No specific software installation is required to view the diagrams. The content is primarily visual.
• Further Information: Detailed textual explanations for each diagram are available in the linked book and repository directory.

Highlighted Details

• Comprehensive coverage of LLM and RL algorithms, including SFT, DPO, PPO, RLHF, GRPO, and various decoding strategies.
• Detailed breakdowns of model components like input/output layers, attention mechanisms, and positional encodings (RoPE, ALiBi).
• Visual explanations of advanced concepts such as Constitutional AI (CAI), Retrieval-Augmented Generation (RAG), and Monte Carlo Tree Search (MCTS).
• Includes a detailed taxonomy of optimization techniques across training and inference stages.

Maintenance & Community

• The repository is maintained by changyeyu, author of "Large Model Algorithms."
• Community engagement is encouraged through GitHub Stars, issue reporting, and pull requests for corrections and improvements.
• Links to Bilibili, Zhihu, and WeChat official accounts are provided for further discussion and engagement.

Licensing & Compatibility

• All diagrams are licensed under a permissive license allowing free use, modification, and redistribution.
• For online use (posts, blogs), attribution to the original author and repository is required.
• For formal publications (papers, books), formal citation is required, and original author information can be removed.
• Non-commercial use is strictly prohibited.

Limitations & Caveats

The repository focuses exclusively on visual explanations and does not provide runnable code or pre-trained models. The depth of detail is tied to the content of the referenced book, "Large Model Algorithms."