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Quark 0.7 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
Save & Load Quantized Models
Exporting Quantized Models
Best Practices for Post-Training Quantization (PTQ)
Debugging quantization degradation
Language Model Optimization
Activation/Weight Smoothing (SmoothQuant)
Block Floating Point 16
Extensions
- Integration with AMD Pytorch-light (APL)
- Brevitas Integration
Using MX (Microscaling)
Two Level Quantization Formats

Advanced Quark Features for ONNX

Configuring ONNX Quantization
Data and OP Types
Accelerate with GPUs
Mixed Precision
Block Floating Point 16 (BFP16)
BF16 Quantization
Microscaling (MX)
Accuracy Improvement Algorithms
Optional Utilities
Tools

APIs

PyTorch APIs
ONNX APIs

Troubleshooting and Support

PyTorch FAQ
ONNX FAQ

quark.torch.algorithm.blockwise_tuning

quark.torch.algorithm.blockwise_tuning

Contents

Package Contents
- Classes

`quark.torch.algorithm.blockwise_tuning`#

Package Contents#

Classes#

Contents

Package Contents
- Classes

Last updated on Feb 11, 2025.

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