Sample List

**Table 1** Samples
Framework	Feature	How to Obtain
PyTorch	Accuracy-based Automatic Quantization	Click auto_calibration to obtain the sample. For details, see the readme file.
	PTQ	Click calibration to obtain the sample. For details, see the readme file.
	QAT	Click retrain to obtain the sample. For details, see the readme file.
	Auto Channel Pruning Search	Click auto_channel_prune to obtain the sample. For details, see the readme file.
	Filter-Level Sparsity	Click channel_prune to obtain the sample. For details, see the readme file.
	2:4 Structured Sparsity	Click selective_prune to obtain the sample. For details, see the readme file.
	Compression Combination	Click mix_compression to obtain the sample. For details, see the readme file.
	Tensor Decomposition	Click tensor_decompose to obtain the sample. For details, see the readme file.
	QAT in Single-Operator Mode	Click retrain_qat_op to obtain the sample. For details, see the readme file.
	Layer-wise Distillation	Click distillation to obtain the sample. For details, see the readme file.
	ADA Adaptive Rounding Quantization	Click ada_round_calibration to obtain the sample. For details, see the readme file.
PyTorch	KV Cache Quantization	Click kv_cache_quantization to obtain the sample. For details, see the readme file.
PyTorch	Torch API-based PTQ	Full quantization HiF8/FP8 Calibration Quantization Click hif8_fp8_calibration to obtain the sample. For details, see the readme file. Weight-only quantization HIF8/FP8 weight-only quantization Click hif8_fp8_weight_quantization to obtain the sample. For details, see the readme file. AWQ MXFP4 weight quantization Click mxfp4_quantization to obtain the sample. For details, see the readme file. 4-bit LUT weight quantization Click lut4_quantization to obtain the sample. For details, see the readme file. FP4 weight-only quantization Click fp4_weight_quantization to obtain the sample. For details, see the readme file.
ONNX	CLI-based Quantization PTQ using the CLI QAT model adaptation to CANN format using the CLI	Click cmd to obtain the sample. For details, see the readme file.
	Accuracy-based Automatic Quantization	Click accuracy_based_auto_calibration to obtain the sample. For details, see the readme file for accuracy-based automatic quantization.
	PTQ	Click calibration to obtain the sample. For details, see the readme file for post-training quantization.
	QAT Model Adaptation to CANN Format	Click convert_qat2ascend to obtain the sample. For details, see the readme file for converting a QAT model to a CANN model.
TensorFlow	CLI-based Quantization PTQ using the CLI QAT model adaptation to CANN format using the CLI	Click cmd to obtain the sample. For details, see the readme file.
	Accuracy-based Automatic Quantization	Click auto_calibration to obtain the sample. For details, see the readme file.
	PTQ	Click calibration to obtain the sample. For details, see the readme file.
	QAT	Click retrain to obtain the sample. For details, see the readme file.
	Auto Channel Pruning Search	Click auto_channel_prune to obtain the sample. For details, see the readme file.
	Filter-Level Sparsity (Manual Sparsity)	Click channel_prune to obtain the sample. For details, see the readme file.
	2:4 Structured Sparsity	Click selective_prune to obtain the sample. For details, see the readme file.
	Compression Combination	Click mix_compression to obtain the sample. For details, see the readme file.
	Tensor Decomposition	Click tensor_decompose to obtain the sample. For details, see the readme file.
	Model Adaptation Using convert_model API	Click convert_model to obtain the sample. For details, see the readme file.
	QAT Model Adaptation to CANN Format	Click convert_qat2ascend to obtain the sample. For details, see the readme file.
Caffe	CLI-based Quantization	Click cmd to obtain the sample. For details, see the readme file.
	Accuracy-based Automatic Quantization	Click auto_calibration to obtain the sample. For details, see the readme file.
	PTQ	Click calibration to obtain the sample. For details, see the readme file.
	QAT	Click retrain to obtain the sample. For details, see the readme file.
	Tensor Decomposition	Click tensor_decompose to obtain the sample. For details, see the readme file.
	Model Adaptation	Click convert_model to obtain the sample. For details, see the readme file.
TensorFlow, Ascend	MobileNetV2	Post-training quantization of the classification network model. Click amct_tensorflow_ascend to obtain the sample from the mobilenetv2 directory. For details, see the readme file.
TensorFlow, Ascend	YOLOv3	Post-training quantization of the detection network model. Click amct_tensorflow_ascend to obtain the sample from the yolov3 directory. For details, see the readme file.