🧠 QuantLLM: Efficient GGUF Model Quantization and Deployment

📌 Overview

QuantLLM is a Python library designed for efficient model quantization using the GGUF (GGML Universal Format) method. It provides a robust framework for converting and deploying large language models with minimal memory footprint and optimal performance. Key capabilities include:

• Memory-efficient GGUF quantization with multiple precision options (2-bit to 8-bit)
• Chunk-based processing for handling large models
• Comprehensive benchmarking tools
• Detailed progress tracking with memory statistics
• Easy model export and deployment

🎯 Key Features

Feature	Description
✅ Multiple GGUF Types	Support for various GGUF quantization types (Q2_K to Q8_0) with different precision-size tradeoffs
✅ Memory Optimization	Chunk-based processing and CPU offloading for efficient handling of large models
✅ Progress Tracking	Detailed layer-wise progress with memory statistics and ETA
✅ Benchmarking Tools	Comprehensive benchmarking suite for performance evaluation
✅ Hardware Optimization	Automatic device selection and memory management
✅ Easy Deployment	Simple conversion to GGUF format for deployment
✅ Flexible Configuration	Customizable quantization parameters and processing options

🚀 Getting Started

Installation

Basic installation:

pip install quantllm

With GGUF support (recommended):

pip install quantllm[gguf]

Quick Example

from quantllm import QuantLLM
from transformers import AutoTokenizer

# Load tokenizer and prepare data
model_name = "facebook/opt-125m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
calibration_text = ["Example text for calibration."] * 10
calibration_data = tokenizer(calibration_text, return_tensors="pt", padding=True)["input_ids"]

# Quantize model
quantized_model, benchmark_results = QuantLLM.quantize_from_pretrained(
    model_name_or_path=model_name,
    bits=4,                    # Quantization bits (2-8)
    group_size=32,            # Group size for quantization
    quant_type="Q4_K_M",      # GGUF quantization type
    calibration_data=calibration_data,
    benchmark=True,           # Run benchmarks
    benchmark_input_shape=(1, 32)
)

# Save and convert to GGUF
QuantLLM.save_quantized_model(model=quantized_model, output_path="quantized_model")
QuantLLM.convert_to_gguf(model=quantized_model, output_path="model.gguf")

For detailed usage examples and API documentation, please refer to our:

• 📚 Official Documentation
• 🎓 Tutorials
• 📖 API Reference

💻 Hardware Requirements

Minimum Requirements

• CPU: 4+ cores
• RAM: 16GB+
• Storage: 10GB+ free space
• Python: 3.10+

Recommended for Large Models

• CPU: 8+ cores
• RAM: 32GB+
• GPU: NVIDIA GPU with 8GB+ VRAM
• CUDA: 11.7+
• Storage: 20GB+ free space

GGUF Quantization Types

Type	Bits	Description	Use Case
Q2_K	2	Extreme compression	Size-critical deployment
Q3_K_S	3	Small size	Limited storage
Q4_K_M	4	Balanced quality	General use
Q5_K_M	5	Higher quality	Quality-sensitive tasks
Q8_0	8	Best quality	Accuracy-critical tasks

🔄 Version Compatibility

QuantLLM	Python	PyTorch	Transformers	CUDA
1.2.0	≥3.10	≥2.0.0	≥4.30.0	≥11.7

🗺 Roadmap

• Support for more GGUF model architectures
• Enhanced benchmarking capabilities
• Multi-GPU processing support
• Advanced memory optimization techniques
• Integration with more deployment platforms
• Custom quantization kernels

🤝 Contributing

We welcome contributions! Please see our CONTRIBUTE.md for guidelines and setup instructions.

📝 License

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

🙏 Acknowledgments

• llama.cpp for GGUF format
• HuggingFace for Transformers library
• CTransformers for GGUF support

📫 Contact & Support

• GitHub Issues: Create an issue
• Documentation: Read the docs
• Discord: Join our community
• Email: support@quantllm.ai