Introduction

This project provides calling samples for different scenarios. After setting up the environment, you can try to run them according to actual scenarios:

Sample	Algorithm	Description
Quantize model using MIN-MAX algorithm	Min-Max	Simple quantization based on extrema, best for beginners
Quantize model using AWQ algorithm	AWQ	Activation-aware weight quantization, suitable for large model PTQ
Quantize model using GPTQ algorithm	GPTQ	Weight quantization based on second-order information, layer-by-layer optimization
Quantize model using SmoothQuant algorithm	SmoothQuant	W8A8 quantization that smooths activation distribution
Quantize model using Cast direct conversion algorithm	Cast	HiFloat8 data direct conversion
Quantize model using Quantile algorithm	Quantile	HiFloat8 quantile quantization
Quantize model using OFMR algorithm	OFMR	Output feature Min-Max quantization
Quantize model using MXQuant algorithm	MXQuant	Micro-scaling floating-point quantization (MXFP8/MXFP4)
Quantize model using FlatQuant algorithm (Experimental)	FlatQuant	Quantization that flattens distribution through affine transformation

Note: Samples marked as "Experimental" depend on content under amct_pytorch/experimental/. Build the package with bash build.sh --torch --experimental (or --pkg --experimental) before use.