mono-quant 1.0.1

Simple, reliable model quantization with minimal dependencies

Mono Quant

Ultra-lightweight, model-agnostic quantization for PyTorch

What is Mono Quant?

Mono Quant is a simple, reliable model quantization package for PyTorch with minimal dependencies. Just torch and numpy, no bloat.

Key Features

• Model-Agnostic - Works with any PyTorch model: HuggingFace, local, or custom
• Multiple Modes - INT8, INT4, and FP16 quantization
• Flexible Calibration - Dynamic (no data) or static (with calibration data)
• Robust Validation - SQNR metrics, size comparison, and accuracy warnings
• Dual Interface - Python API for automation, CLI for CI/CD
• Build-Phase Only - Quantize during build, deploy lightweight models

Installation

pip install mono-quant

Requirements

• Python 3.11 or higher
• PyTorch 2.0 or higher
• NumPy 1.24 or higher

Quick Start

Python API

from mono_quant import quantize

# Dynamic INT8 quantization (no calibration data needed)
result = quantize(model, bits=8, dynamic=True)

# Save the quantized model
result.save("model_quantized.pt")

# Check metrics
print(f"Compression: {result.info.compression_ratio:.2f}x")
print(f"SQNR: {result.info.sqnr_db:.2f} dB")

CLI

# Dynamic quantization
monoquant quantize --model model.pt --bits 8 --dynamic

# With custom output path
monoquant quantize --model model.pt --bits 8 --output model_quantized.pt

Quantization Modes

Dynamic Quantization (Fastest, No Data)

result = quantize(model, bits=8, dynamic=True)

Static Quantization (Best Accuracy, Requires Data)

calibration_data = [torch.randn(1, 3, 224, 224) for _ in range(150)]

result = quantize(
    model,
    bits=8,
    dynamic=False,
    calibration_data=calibration_data
)

INT4 Quantization (Maximum Compression)

result = quantize(
    model,
    bits=4,
    dynamic=False,
    calibration_data=calibration_data,
    group_size=128  # Default
)

Documentation

Full documentation available at https://thataverageguy.github.io/mono-quant

• Installation Guide
• Quick Start
• User Guide
• CLI Reference
• API Reference

Why Mono Quant?

Most quantization tools are tied to specific frameworks (HuggingFace, TFLite) or require heavy dependencies. Mono Quant fills the niche of "just quantize the weights, nothing else."

Design Philosophy

Aspect	Approach
Model Loading	You load the model, we quantize it
Dependencies	Only torch and numpy required
Use Case	Build-phase (CI/CD, local development)
Scope	Quantization only, no runtime or serving

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

Links

• GitHub: https://github.com/thatAverageGuy/mono-quant
• PyPI: https://pypi.org/project/mono-quant/
• Documentation: https://thataverageguy.github.io/mono-quant
• Issues: https://github.com/thatAverageGuy/mono-quant/issues