A Chinese-language tutorial project that translates, reproduces, and localizes Andrew Ng's large language model courses. It provides runnable Jupyter notebooks covering prompt engineering, RAG, and fine-tuning for Chinese developers.
Project overview
The project addresses language and network access barriers by providing tested Chinese prompts and localized learning materials with smooth domestic China access.
Project type
AI Agent · RAG · Prompt Engineering · Learning Resources
Use cases
Knowledge Q&A · Learning & Education
Deployment
Refer to project documentation
License
License pending
Best for
Chinese-speaking developers needing translated, runnable educational materials on prompt engineering, RAG, and fine-tuning.
Educators looking for tested Chinese prompt examples that achieve roughly equivalent performance to English versions.
Key capabilities
Provides bilingual reproduced code notebooks from original courses that users can run, updated frequently.
Provides web-based online reading versions and downloadable PDF versions of the mandatory courses.
Implements and tests Chinese prompts that achieve roughly equivalent performance to English prompts.
Includes courses on how to use Gradio to quickly build user interfaces for generative AI applications via Python.
Includes tutorials on building agents using LangChain's syntax for Functions, Tools, and Agents.
Teaches how to efficiently fine-tune open-source large language models locally on personal data using the Lamini framework.
Provides links to bilingual subtitle videos and video explanations for the Prompt Engineering course.
Limitations and risks
Running the code notebooks requires at least one LLM API, preferably OpenAI. Data cannot remain entirely local and requires external services.
Using the OpenAI API or other LLM APIs may incur paid service costs.
Getting started
Setup difficulty is medium. Users must obtain at least one LLM API key, clone the repository or download the notebooks, and open them in a Jupyter Notebook environment.
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