Personal notes documenting my exploration, implementation, and research on Large Language Models (LLMs).
This repository contains a collection of IPython Notebook (.ipynb) memos related to large language models and modern AI systems. The notebooks are mainly used to record my learning process, technical experiments, paper reading notes, implementation details, and research-oriented reflections.
Many of the notebooks are written or translated in Chinese with the assistance of LLMs, so the contents should be understood as personal study memos rather than polished tutorials or formal publications.
The repository may include notes on topics from NLP such as:
- Large language model architectures
- Tokenization and text compression
- BPE and subword modeling
- Positional encoding and RoPE
- Transformer implementation details
- Multimodal models
- Vision-Language-Action models
- Reinforcement learning for LLMs
- AI agent systems
- Research notes and technical summaries
The goal of this repository is to build a personal knowledge base for understanding LLMs from both theoretical and implementation perspectives. The notebooks are intended to help organize concepts, reproduce important ideas, and connect different areas of AI research.
Some notebooks may be translated, rewritten, or reorganized with the help of LLM-based tools. As a result, terminology, explanations, or code comments may not always be fully standardized. Important technical details should be checked carefully before being used for research, teaching, or engineering work.
This repository is mainly for personal learning and research documentation. The contents may contain incomplete explanations, experimental code, or translation inaccuracies.