Project Title

dive-into-llms — Comprehensive Programming Tutorials for Large Language Models

Overview

"Dive into LLMs" is a series of programming practice tutorials focused on large language models (LLMs). Originating from course materials at Shanghai Jiao Tong University, this project aims to provide an accessible entry point for students and researchers to understand and work with LLMs through practical exercises. The tutorials are freely available and cover a range of topics from fine-tuning to advanced applications like mathematical reasoning and watermarking.

Key Features

Comprehensive Coverage: Tutorials cover a wide array of topics related to LLMs, from basic operations to advanced techniques.
Practical Exercises: Each tutorial includes hands-on Jupyter Notebook exercises to reinforce learning.
Open Source and Community-Driven: The project encourages contributions and is built on community feedback and collaboration.

Use Cases

Educational Purposes: Students and educators can use these tutorials to understand the practical applications of LLMs in natural language processing.
Research and Development: Researchers can leverage the tutorials for their studies on LLMs and to prototype new ideas.
Professional Development: Practitioners in the field can use the tutorials to upskill and stay current with the latest in LLM technology.

Advantages

Accessibility: The tutorials are designed to be approachable for beginners, making complex topics more digestible.
Up-to-Date Content: Regular updates ensure that the material remains relevant with the latest developments in LLMs.
Community Support: Active community engagement ensures that the tutorials are constantly improved and expanded.

Limitations / Considerations

Advanced Knowledge Required: While beginner-friendly, some topics may require a foundational understanding of machine learning and programming.
Resource Intensive: Working with LLMs can be computationally demanding, which might be a barrier for those with limited resources.

Hugging Face Transformers: A library of state-of-the-art pre-trained models for NLP, differing in that it provides a wide range of models and is not tutorial-based.
Google's BERT Research: A project that introduced the BERT model, which is foundational to many LLMs; it is more research-focused than a tutorial project.
Stanford's NLP Course: Offers a comprehensive course on natural language processing, including sections on LLMs, with a more academic approach.

Basic Information

GitHub: https://github.com/Lordog/dive-into-llms
Stars: 9,696
License: Unknown
Last Commit: 2025-11-13

📊 Project Information

Project Name: dive-into-llms
GitHub URL: https://github.com/Lordog/dive-into-llms
Programming Language: Jupyter Notebook
⭐ Stars: 9,696
🍴 Forks: 977
📅 Created: 2024-04-08
🔄 Last Updated: 2025-11-13

🏷️ Project Topics

Topics: [, ]

📚 Documentation

张倬胜

This article is automatically generated by AI based on GitHub project information and README content analysis

dive-into-llms

Project Description