This repository provides practical code examples and supplementary materials for the book "ChatGPT Principles and Practice: Algorithms, Technologies, and Privatization of Large Language Models." It targets engineers and researchers seeking hands-on experience with LLM implementation, offering code for tasks like UniLM, sentiment analysis, information extraction, text summarization, and PPO-based reinforcement learning.

How It Works

The project implements various LLM techniques discussed in the book, including UniLM for unified language tasks, prompt-based sentiment analysis, and GPT-2 for text summarization. It also features practical applications of PPO for fine-tuning language generation models and code for building ChatGPT-like systems for question-answering from documents. Supplementary content covers newer advancements like Llama2 and BaiChuan2.

Quick Start & Requirements

• Installation typically involves cloning the repository and setting up Python environments.
• Prerequisites include Python and libraries like Hugging Face Transformers, PyTorch, and potentially CUDA for GPU acceleration. Specific requirements vary per chapter.
• Links to official documentation or demos are not explicitly provided, but the book's content guides usage.

Highlighted Details

• Code examples cover diverse LLM applications from sentiment analysis to document-based QA.
• Includes practical implementations of reinforcement learning (PPO) for model fine-tuning.
• Supplementary sections address rapidly evolving LLM technologies like Llama2 and BaiChuan2.
• A dedicated section for errata and corrections to the published book is maintained.

Maintenance & Community

The project is maintained by the author, Liu Cong (刘聪NLP), with contact via email (logcongcong@gmail.com) and a presence on Zhihu. Community feedback is encouraged via GitHub issues for errata.

Licensing & Compatibility

The repository's license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking would require clarification of the licensing terms.

Limitations & Caveats

Some chapters are marked as "to be supplemented," indicating incomplete content. The rapid pace of LLM development means some book content may be outdated, though supplementary sections aim to address this. Specific hardware requirements (e.g., GPUs) may be necessary for running certain examples efficiently.