Here I take the 14B DeepSeek-R1-Distill-Qwen-14B as an example and make an inference comparison with Microsoft's Phi-4-14B Quantization conversion olive auto-opt --model_name_or_path <Your Phi-4-14B or DeepSeek-R1-Distill-Qwen-14B local path > --output_path <Your converted Phi-4-...
Calibration Dataloader (Needed for static quantization) Evaluation Dataloader Evaluation MetricBelow is an example of how to enable Intel® Neural Compressor on MobileNet_v2 with built-in data loader, dataset, and metric1. Prepare quantization environment# bash commandpip i...
It provides web-based UI service to make quantization easier and supports code-based usage for more abundant quantization settings. Refer to bench document for how to use web-based UI service and example document for a simple code-based demo. Usage There are multiple ways to access the ONNX...
add_argument('--quantization_mode', default='Integer', choices=('Integer', 'QLinear')) parser.add_argument('--static', '-s', action='store_true', default=False) parser.add_argument('--asymmetric_input_types', action='store_true', default=False) parser.add_argument('--input_...
ONNX Runtime quantizationis applied to further reduce the size of the model. When deploying the GPT-C ONNX model, the IntelliCode client-side model service retrieves the output tensors from ONNX Runtime and sends them back for the next inference step until all beams...
It facilitates performance tuning to run models cost-efficiently on the target hardware and has support for features like quantization and hardware acceleration, making it one of the ideal choices for deploying efficient, high-performance ML applications. For examples of how ONNX mode...
Example: HETERO:MYRIAD,CPU HETERO:HDDL,GPU,CPU MULTI:MYRIAD,GPU,CPU For more information on OpenVINO Execution Provider's ONNX Layer support, Topology support, and Intel hardware enabled, please refer to the document OpenVINO-ExecutionProvider.md in $onnxruntime_root/docs/execution_providers NUPHAR...
dynamic quantization: quantize fp32 weight to int8 during quantization phase , compute quant params (scale and zero point) on the fly which will increase performance overhead when doing inference but its accuracy may be a little bit higher. static quantization: same as dynamic quantization, it q...
INT8 models are generated byIntel® Neural Compressor.Intel® Neural Compressoris an open-source Python library which supports automatic accuracy-driven tuning strategies to help user quickly find out the best quantized model. It implements dynamic and static quantization for ONNX models and can ...
static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the first output of `then_branch` is typed float tensor...