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Quark Version List

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Quark 0.6.0 documentation

Release Notes

Release Information

Getting Started with Quark

Introduction to Quantization
Installation
Basic Usage
- Quark for PyTorch
- Quark for ONNX
Accessing PyTorch Examples
Accessing ONNX Examples

Advanced Quark Features for PyTorch

Configuring PyTorch Quantization
Calibration Methods
Calibration Datasets
Quantization Strategies
Quantization Schemes
Quantization Symmetry
Language Model Optimization
Exporting Quantized Models
- Bridge from Quark to llama.cpp
Exporting Using ONNX Runtime Gen AI Model Builder
Extensions
- Integration with AMD Pytorch-light (APL)
- Brevitas Integration
Using MX (Microscaling)
Two Level Quantization Formats

Advanced Quark Features for ONNX

Configuring ONNX Quantization
Calibration methods
Calibration datasets
Quantization Strategies
Quantization Schemes
Quantization Symmetry
Mixed Precision
Block Floating Point 16 (BFP16)
Microscaling (MX)
Accuracy Improvement Algorithms
Optional Utilities
Tools

APIs

PyTorch APIs
ONNX APIs

Troubleshooting and Support

PyTorch FAQ
ONNX FAQ

Accuracy Improvement Algorithms

Accuracy Improvement Algorithms

Accuracy Improvement Algorithms#

Quark for ONNX provides several techniques to improve the accuracy for quantized models after PTQ.

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Microscaling (MX)

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Quantizing Using CrossLayerEqualization (CLE)

Last updated on Dec 10, 2024.

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