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Contributing to Large Language Models Tutorial

Thank you for your interest in contributing! 🎉 This project aims to make LLM learning accessible to everyone.

🚀 Quick Start

  1. Star ⭐ this repository if you find it helpful
  2. Fork 🍴 the repository to your account
  3. Clone your fork locally
  4. Create a new branch for your feature
  5. Make your changes
  6. Test your notebooks
  7. Submit a pull request

🛠️ How to Contribute

Adding New Tutorials

  1. Tutorial Structure:

    Tutorial - [Topic Name].ipynb

  2. Required Sections:

    • Clear title and description
    • Learning objectives
    • Prerequisites
    • Step-by-step code with explanations
    • Expected outputs
    • Next steps/further reading

  3. Code Quality:

    • Test all code cells
    • Include error handling
    • Add comments for complex logic
    • Use consistent formatting

Improving Existing Content

  • Fix typos and grammatical errors
  • Improve code efficiency
  • Add better explanations
  • Update deprecated libraries
  • Enhance visualizations

Documentation

  • Improve README sections
  • Add setup instructions
  • Create troubleshooting guides
  • Write FAQ entries

📝 Guidelines

Notebook Standards

  • Clear Objectives: Start each notebook with learning goals
  • Executable Code: All cells must run without errors
  • Explanations: Balance code with clear explanations
  • Dependencies: List all required packages
  • Outputs: Include expected outputs for key cells

Code Style

  • Follow PEP 8 for Python code
  • Use meaningful variable names
  • Add docstrings for functions
  • Keep cells focused and concise
  • Test with fresh kernel

Content Guidelines

  • Beginner-Friendly: Assume minimal prior knowledge
  • Practical: Focus on real-world applications
  • Updated: Use current library versions
  • Ethical: Follow AI ethics best practices

🐛 Reporting Issues

Bug Reports

Include:

  • Tutorial name and cell number
  • Error message (full traceback)
  • Python version and environment
  • Expected vs actual behavior

Feature Requests

Include:

  • Clear description of the feature
  • Use case or motivation
  • Possible implementation approach

🎯 Good First Issues

Look for issues labeled:

  • good first issue
  • documentation
  • beginner-friendly
  • help wanted

📊 Review Process

  1. Automated Checks: Ensure notebooks run without errors
  2. Code Review: Maintainer reviews code quality
  3. Content Review: Check educational value
  4. Testing: Verify on different environments

🏆 Recognition

Contributors are recognized:

  • In the README contributors section
  • In release notes
  • On social media shout-outs

📞 Getting Help

  • GitHub Discussions: For questions and ideas
  • Issues: For bugs and feature requests
  • Discord: Join our community

📋 Checklist

Before submitting:

  • Notebook runs from start to finish
  • All dependencies listed
  • Clear explanations included
  • Code follows style guidelines
  • Tests pass (if applicable)
  • Documentation updated

🙏 Thank You

Every contribution helps make LLM education more accessible. Whether it's a small typo fix or a major tutorial, your help is appreciated!

Questions? Open an issue or reach out on Twitter!