Thank you for your interest in contributing to the LLM Optimization Toolkit! This project aims to be the world's most comprehensive resource for LLM optimization, and we welcome contributions from researchers, developers, and enthusiasts at all levels.
- Novel Quantization Methods: Implement new quantization algorithms
- Abliteration Techniques: Develop advanced model modification methods
- Paper Implementations: Faithful reproductions of research papers
- Benchmarking Studies: Comprehensive evaluation of existing methods
- Theoretical Analysis: Mathematical foundations and proofs
- Code Optimizations: Performance improvements and bug fixes
- New Features: CLI tools, APIs, and utilities
- Platform Support: Compatibility with different hardware/software
- Documentation: Tutorials, guides, and API documentation
- Testing: Unit tests, integration tests, and benchmarks
- Tutorials: Step-by-step learning materials
- Examples: Practical use cases and demonstrations
- Visualizations: Charts, graphs, and interactive content
- Translations: Multi-language documentation
- Community Support: Answering questions and helping users
# Clone the repository
git clone https://github.com/your-repo/llm-optimization
cd llm-optimization
# Create development environment
conda create -n llm-opt-dev python=3.10
conda activate llm-opt-dev
# Install development dependencies
pip install -r requirements-dev.txt
pip install -e .
# Set up pre-commit hooks
pre-commit installCreate requirements-dev.txt:
# Core requirements
-r requirements.txt
# Development tools
pytest>=7.0.0
pytest-cov>=4.0.0
black>=22.0.0
flake8>=5.0.0
mypy>=0.991
pre-commit>=2.20.0
sphinx>=5.0.0
sphinx-rtd-theme>=1.0.0
# Jupyter and interactive tools
jupyter>=1.0.0
jupyterlab>=3.0.0
streamlit>=1.25.0
plotly>=5.15.0
# Additional testing tools
hypothesis>=6.0.0
factory-boy>=3.2.0
responses>=0.21.0
llm-optimization/
├── llm_toolkit/ # Core CLI and API
├── advanced_quantization/ # Research implementations
├── research_extensions/ # Novel research
├── educational_content/ # Learning materials
├── benchmarks/ # Evaluation suites
├── examples/ # Usage examples
├── docs/ # Documentation
├── tests/ # Test suites
└── scripts/ # Utility scripts
- Python: Follow PEP 8, use Black formatter
- Type Hints: Required for all public functions
- Docstrings: Google-style docstrings for all modules, classes, and functions
- Comments: Explain complex algorithms and research concepts
def quantize_model(
model: nn.Module,
method: str,
bits: int = 4,
calibration_data: Optional[List[str]] = None
) -> Tuple[nn.Module, Dict[str, Any]]:
"""
Quantize a language model using specified method.
Args:
model: The model to quantize
method: Quantization method ('gptq', 'awq', 'qlora')
bits: Number of quantization bits
calibration_data: Optional calibration dataset
Returns:
Tuple of (quantized_model, metrics)
Raises:
ValueError: If method is not supported
Example:
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> quantized, metrics = quantize_model(model, "qlora", bits=4)
"""- Unit Tests: All new functions must have unit tests
- Integration Tests: End-to-end testing for major features
- Benchmark Tests: Performance regression testing
- Coverage: Minimum 80% code coverage for new code
# tests/test_quantization.py
import pytest
from llm_toolkit.quantization import QuantizationCLI
class TestQuantizationCLI:
def test_qlora_quantization(self):
"""Test QLoRA quantization functionality."""
cli = QuantizationCLI()
# Test implementation
@pytest.mark.gpu
def test_gpu_quantization(self):
"""Test GPU-specific quantization features."""
# GPU-specific tests- API Documentation: Auto-generated from docstrings
- Tutorials: Jupyter notebooks with explanations
- Research Documentation: Mathematical formulations and references
- User Guides: Step-by-step instructions
- Faithful Reproduction: Implement exactly as described in papers
- Reference Implementation: Include links to original code if available
- Validation: Reproduce paper results within acceptable tolerance
- Documentation: Explain algorithms step-by-step
"""
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Based on: Frantar et al. (2022) - https://arxiv.org/abs/2210.17323
This implementation faithfully reproduces the GPTQ algorithm with the following key components:
1. Layer-wise quantization to minimize error propagation
2. Optimal Brain Surgeon (OBS) based weight updates
3. Efficient GPU implementation for large models
Key differences from original implementation:
- Updated for latest PyTorch version
- Added support for newer model architectures
- Improved memory efficiency
"""- Literature Review: Cite related work and explain novelty
- Theoretical Foundation: Provide mathematical justification
- Experimental Validation: Comprehensive evaluation
- Reproducibility: Include all code and data needed to reproduce results
- Standardized Metrics: Use established evaluation protocols
- Multiple Datasets: Test on diverse datasets
- Statistical Significance: Report confidence intervals
- Hardware Specifications: Document testing environment
- Fork the repository and create a feature branch
- Write tests for your changes
- Ensure all tests pass locally
- Run code formatting and linting
- Update documentation as needed
- Add entry to CHANGELOG.md
# Pre-submission checklist
pytest tests/
black .
flake8 .
mypy llm_toolkit/
python -m llm_toolkit --help # Test CLI works## Description
Brief description of changes and motivation.
## Type of Change
- [ ] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
- [ ] Research contribution (new algorithm or paper implementation)
- [ ] Documentation update
## Research Context (if applicable)
- Paper reference: [Title, Authors, Year, Link]
- Key contributions: [List main innovations]
- Validation: [How results were verified]
## Testing
- [ ] Unit tests added/updated
- [ ] Integration tests pass
- [ ] Benchmarks run successfully
- [ ] Manual testing completed
## Checklist
- [ ] Code follows style guidelines
- [ ] Self-review completed
- [ ] Documentation updated
- [ ] Tests added for new functionality
- [ ] All tests pass
- [ ] No breaking changes (or clearly documented)- Automated Checks: CI/CD pipeline runs tests and checks
- Code Review: Maintainers review code quality and design
- Research Review: For research contributions, domain experts review
- Integration Testing: Full system testing with new changes
- Documentation Review: Ensure documentation is complete and accurate
- Contributors File: All contributors listed in CONTRIBUTORS.md
- Release Notes: Major contributions highlighted in releases
- Research Credits: Academic contributors credited in papers/documentation
- Community Highlights: Notable contributions featured in community updates
For research contributions:
- Original paper authors always credited
- Implementation contributors credited as "Implementation by [Name]"
- Novel research contributions eligible for co-authorship on related publications
- Conference presentations and workshops opportunities
-
Advanced Quantization Methods
- GPTQ improvements and optimizations
- AWQ enhancements and new variants
- Novel quantization algorithms
- Hardware-specific optimizations
-
Abliteration Research
- Selective abliteration techniques
- Quality preservation methods
- Ethical considerations and safeguards
- Evaluation metrics
-
Multi-Modal Optimization
- Vision-language model quantization
- Cross-modal efficiency techniques
- Novel architectures support
-
Educational Content
- Interactive tutorials
- Video explanations
- Beginner-friendly guides
- Advanced research tutorials
- Quantization Theory: Mathematical foundations and theoretical analysis
- Hardware Optimization: GPU, TPU, and edge device optimizations
- Quality Metrics: Better evaluation methods for quantized models
- Automated Optimization: ML-based quantization parameter selection
- Distributed Quantization: Multi-GPU and multi-node optimization
# VS Code extensions
code --install-extension ms-python.python
code --install-extension ms-python.black-formatter
code --install-extension ms-python.flake8
code --install-extension ms-python.mypy-type-checker
# Jupyter setup
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install plotlywidget# Create feature branch
git checkout -b feature/new-quantization-method
# Make changes and commit
git add .
git commit -m "feat: implement novel quantization method
- Add XYZ quantization algorithm
- Include comprehensive tests
- Update documentation"
# Push and create PR
git push origin feature/new-quantization-method# Run specific test categories
pytest tests/unit/ # Unit tests
pytest tests/integration/ # Integration tests
pytest tests/benchmarks/ # Benchmark tests
pytest -m "not slow" # Skip slow tests
pytest --cov=llm_toolkit # Coverage report- GitHub Issues: Bug reports and feature requests
- GitHub Discussions: General questions and community discussions
- Discord Server: Real-time chat and collaboration
- Email: research@llm-optimization.org for research collaborations
- New Contributors: Paired with experienced contributors
- Research Guidance: Academic mentors for research contributions
- Code Reviews: Detailed feedback and learning opportunities
- Career Development: Networking and professional growth
- Development Guide: Detailed technical documentation
- Research Papers: Curated list of relevant papers
- Video Tutorials: Development and research tutorials
- Office Hours: Weekly community calls
- Monthly Research Seminars: Present and discuss latest research
- Quarterly Hackathons: Collaborative development events
- Annual Conference: LLM Optimization Summit
- Workshop Series: Hands-on learning sessions
- Summer Research Program: Intensive research collaboration
- Student Competitions: Academic challenges and prizes
- Industry Partnerships: Real-world application projects
- Open Source Sprints: Focused development sessions
We are committed to providing a welcoming and inclusive environment for all contributors. Please read our Code of Conduct before participating.
- Respectful Communication: Treat all community members with respect
- Constructive Feedback: Provide helpful and actionable feedback
- Inclusive Environment: Welcome contributors from all backgrounds
- Academic Integrity: Proper attribution and honest representation
- Collaborative Spirit: Work together towards common goals
Thank you for contributing to the LLM Optimization Toolkit! Together, we're building the world's most comprehensive resource for LLM optimization. 🚀