This project provides a comprehensive, open-source tutorial on Large Language Models (LLMs), targeting AI, NLP, and ML researchers and practitioners. It aims to demystify LLM fundamentals, from data preparation and model architecture to training, evaluation, and ethical considerations, serving as a valuable resource for those looking to understand or contribute to the LLM ecosystem.

How It Works

The tutorial is built upon foundational courses from Stanford University and National Taiwan University, augmented by community contributions and updates on cutting-edge LLM knowledge. It systematically covers model construction, training, evaluation, and improvement, incorporating practical code examples to provide both theoretical depth and hands-on experience. The content is structured to progressively build understanding, starting from basic concepts and moving towards advanced topics like distributed training and LLM agents.

Quick Start & Requirements

• Installation: No direct installation command is provided as this is a tutorial/educational resource. Access is via the GitHub repository.
• Prerequisites: Familiarity with AI, NLP, and ML concepts is recommended. Specific code examples may require Python and relevant ML libraries.
• Resources: Access to the repository is free. Running code examples may require computational resources depending on the complexity.
• Links:
  • Llama Family Tutorial: https://github.com/datawhalechina/so-large-lm/blob/main/chapters/Llama%E5%BC%80%E6%BA%90%E5%AE%B6%E6%97%8F%EF%BC%9A%E4%BB%8ELlama-1%E5%88%B0Llama-3.md
  • Self-LLM Course (practical): https://github.com/datawhalechina/self-llm
  • LLM Universe (development): https://github.com/datawhalechina/llm-universe

Highlighted Details

• Covers a wide range of LLM topics, including model architectures (RNN, Transformer, MoE), data handling, training strategies, adaptation methods, distributed training, and ethical/legal considerations.
• Includes dedicated sections on LLM "harmfulness" (bias, toxic content, misinformation) and environmental impact.
• Features a detailed breakdown of the Llama open-source family, from Llama-1 to Llama-3.
• Integrates with other Datawhale open-source courses for practical deployment and development.

Maintenance & Community

• Initiated and led by Chen Andong (Ph.D. candidate at Harbin Institute of Technology).
• Contributors include Zhang Fan (Tianjin University) and Wang Maolin (Huazhong University of Science and Technology).
• Project aims for continuous updates based on community contributions and feedback.

Licensing & Compatibility

• The repository itself is hosted on GitHub, implying standard GitHub terms. Specific licensing for the content is not explicitly stated in the README, but the project's open and educational nature suggests a permissive approach. Compatibility for commercial use would require explicit license verification.

Limitations & Caveats

The project is presented as an evolving educational resource, with an initial version planned for completion within three months. While it aims for comprehensiveness, the rapidly advancing nature of LLMs means content may require frequent updates to remain fully current. The practical application of some concepts may necessitate significant computational resources.