hugging-llm  by datawhalechina

HuggingLLM is an educational project aimed at demystifying Large Language Models (LLMs) like ChatGPT for a broad audience, including those outside of NLP or algorithm specializations. It focuses on practical application and API usage, enabling users to build their own AI-powered products and services, with a particular emphasis on integrating with Chinese LLM APIs.

How It Works

The project provides structured learning modules covering ChatGPT fundamentals, API usage for various tasks (QA, classification, text generation, reasoning), and engineering considerations like evaluation and safety. It emphasizes practical implementation through example code and design patterns, encouraging users to build their own applications or demos based on the learned concepts. The approach prioritizes accessibility and hands-on experience over deep theoretical dives into underlying algorithms.

Quick Start & Requirements

• Installation: Primarily through Python package installation (e.g., pip install zhipuai, pip install dashscope).
• Prerequisites:
  • Ability to use OpenAI API (gpt-3.5-turbo) or domestic LLM APIs.
  • Basic programming foundation or project experience.
  • Recommended learning time: 2-3 days per chapter, with 6-8 hours for most chapters excluding "Text Reasoning."
• Resources: A final project/demo is required.
• Documentation: Project Docs

Highlighted Details

• Focuses on practical API usage for creating new functions and applications.
• Includes detailed background and system design for relevant tasks.
• Provides example code and implementation workflows.
• Offers specific guidance on using domestic LLM APIs like GLM and Qwen.

Maintenance & Community

The project is led by Datawhale members, with core contributors listed. It acknowledges community contributions for bug fixes and updates. Datawhale is an active AI learning community with thousands of members, reachable via their WeChat public account.

Licensing & Compatibility

• License: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
• Compatibility: Non-commercial use is permitted. Commercial use may be restricted by the license terms.

Limitations & Caveats

The project explicitly states it is not designed for users seeking to research underlying algorithms or develop LLMs from scratch. It also notes that practical implementation may involve more complexities than presented, and the content is primarily for introductory guidance.