MLX Model Quantization Toolkit

A comprehensive collection of Jupyter notebooks for converting and quantizing large language models using Apple's MLX framework, optimized for Apple Silicon devices.

🚀 Features

• Universal Model Conversion: Convert any Hugging Face model to MLX format
• Multiple Quantization Methods: Support for AWQ, DWQ, and Dynamic Quantization
• Apple Silicon Optimized: Built specifically for M1/M2/M3/M4 devices
• Automated Workflows: Complete pipeline from download to deployment
• Performance Testing: Built-in benchmarking and validation tools

📋 Requirements

• Hardware: macOS with Apple Silicon (M1/M2/M3/M4)
• Python: 3.8 or higher
• Storage: 50GB+ free space for large models

🛠 Installation

1. Clone the repository:

git clone https://github.com/cs2764/mlx-quantization.git
cd mlx-quantization

2. Install dependencies:

pip install -r requirements.txt

3. Launch Jupyter:

jupyter lab

📚 Notebooks Overview

Core Notebooks

Notebook	Description	Use Case
universal_mlx_converter.ipynb	Universal converter for any HF model	General model conversion
awq_quantization.ipynb	Activation-aware Weight Quantization	High-quality 4-bit quantization
dwq_quantization.ipynb	Distilled Weight Quantization	Fast quantization with good quality
dynamic_quantization.ipynb	Dynamic mixed-precision quantization	Optimal size/quality balance

Quantization Methods Comparison

Method	Speed	Quality	Size Reduction	Best For
AWQ	Medium	High	~75%	Production deployment
DWQ	Fast	Good	~70%	Quick prototyping
Dynamic	Slow	Highest	Variable	Research/experimentation

🔄 Common Workflow

Each notebook follows this standardized pattern:

1. Environment Setup - Dependency installation and MLX verification
2. Model Configuration - Set up directories and parameters
3. Model Download - Fetch original model from Hugging Face
4. Conversion/Quantization - Apply selected quantization method
5. Validation - Test converted model functionality
6. Performance Analysis - Compare speed and quality metrics
7. Optional Upload - Push to Hugging Face Hub
8. Cleanup - Remove temporary files

📁 Directory Structure

mlx-quantization/
├── models/                          # Model storage
│   ├── <model_name>/               # Original models
│   └── <model_name>_<method>_<bits>/ # Quantized outputs
├── sensitivities/                   # Layer analysis files
├── *.ipynb                         # Conversion notebooks
├── requirements.txt                # Dependencies
└── README.md                       # This file

🚀 Quick Start

1. Choose your quantization method based on your requirements
2. Open the corresponding notebook in Jupyter Lab
3. Follow the step-by-step instructions in each cell
4. Monitor the conversion process and review results
5. Test the quantized model before deployment

📊 Performance Benchmarks

Typical results on Apple M2 Pro:

• Model Size Reduction: 60-80% smaller than original
• Inference Speed: 2-4x faster on Apple Silicon
• Quality Retention: 95-99% of original performance
• Memory Usage: 50-75% reduction

🔧 MLX Commands Reference

Basic Conversion

python -m mlx_lm.convert --hf-path <source> --mlx-path <target>

AWQ Quantization

python -m mlx_lm.awq --model <model> --mlx-path <output> --bits 4

DWQ Quantization

python -m mlx_lm.dwq --model <model> --mlx-path <output> --bits 4

Dynamic Quantization

python -m mlx_lm.dynamic_quant --model <model> --mlx-path <output> --target-bpw 4.0

⚠️ Important Notes

• AWQ models require dequantization before MLX conversion (--dequantize flag)
• Use absolute paths - relative paths may cause issues
• Large models need significant storage - ensure adequate disk space
• Test converted models before production deployment
• Conversion time varies based on model size and method

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly on Apple Silicon
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Apple MLX Team for the excellent framework
• Hugging Face for model hosting and tools
• The open-source ML community

📞 Support

• Issues: Report bugs and request features via GitHub Issues
• Discussions: Join community discussions in GitHub Discussions
• Documentation: Refer to individual notebook markdown cells

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Version: 1.0.0
Last Updated: 2025-01-30
Compatibility: Apple Silicon (M1/M2/M3/M4) + macOS

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