This repository provides a structured Obsidian knowledge base for long-term learning and review of Large Language Models (LLMs). It targets engineers and researchers seeking to build a reusable knowledge network covering LLM concepts, papers, frameworks, and experimental experience, offering a systematic approach to understanding the LLM landscape.

How It Works

The project is organized as an Obsidian vault, facilitating a networked approach to knowledge management. It employs a layered structure, starting with a "Topic Map" for navigation, leading to detailed "Concept Notes," "Project Notes," "Experiments/Logs," and "Source Material" (papers, reports). This design prioritizes thematic routing and concept entry over direct data dumping, creating a navigable knowledge graph.

Quick Start & Requirements

To use this repository, simply open the root directory with Obsidian. No specific installation commands or non-default prerequisites are mentioned beyond the Obsidian application itself.

Highlighted Details

• Comprehensive coverage of core LLM areas including Transformer architecture, pre-training, parameter-efficient fine-tuning (PEFT) methods like LoRA, and post-training alignment techniques (RLHF, DPO).
• Detailed sections on inference optimization (KV Cache, Flash Attention, vLLM) and training system engineering (FSDP, Ray).
• Includes notes on specific models and frameworks such as Qwen2.5, DeepSeek-V4, EAGLE-3, ReAct, and vLLM.
• Utilizes Obsidian Canvas for visual knowledge mapping, with generated SVG previews for GitHub compatibility.

Maintenance & Community

The repository outlines maintenance rules in AGENTS.md. No specific community links (e.g., Discord, Slack) or roadmap details are provided.

Licensing & Compatibility

This repository has not specified an open-source license. Consequently, its contents should not be considered freely redistributable material without explicit permission, potentially restricting commercial use or integration into closed-source projects.

Limitations & Caveats

The primary limitation is the absence of a defined license, creating ambiguity regarding usage rights and redistribution. PDF source materials are intentionally excluded from the repository. The project appears to be a personal learning and organization effort rather than a formally supported open-source project.