Conversion Parameters#

This document describes all available parameters for openvino.convert_model, ovc, and openvino.save_model without focusing on a particular framework model format. Use this information for your reference as a common description of the conversion API capabilities in general. Part of the options can be not relevant to some specific frameworks. Use Supported Model Formats page for more dedicated framework-dependent tutorials.

You can obtain a model from Hugging Face. When you need to convert it, in most cases, you can use the following simple syntax:

import openvino as ov

ov_model = ov.convert_model('path_to_your_model')
# or, when model is a Python model object
ov_model = ov.convert_model(model)

# Optionally adjust model by embedding pre-post processing here...

ov.save_model(ov_model, 'model.xml')
ovc path_to_your_model

Providing just a path to the model or model object as openvino.convert_model argument is frequently enough to make a successful conversion. However, depending on the model topology and original deep learning framework, additional parameters may be required, which are described below.

  • example_input parameter available in Python openvino.convert_model only is intended to trace the model to obtain its graph representation. This parameter is crucial for converting PyTorch models and may sometimes be required for TensorFlow models. For more details, refer to the PyTorch Model Conversion or TensorFlow Model Conversion.

  • input parameter to set or override shapes for model inputs. It configures dynamic and static dimensions in model inputs depending on your inference requirements. For more information on this parameter, refer to the Setting Input Shapes guide.

  • output parameter to select one or multiple outputs from the original model. This is useful when the model has outputs that are not required for inference in a deployment scenario. By specifying only necessary outputs, you can create a more compact model that infers faster.

  • compress_to_fp16 parameter that is provided by ovc CLI tool and openvino.save_model Python function, gives controls over the compression of model weights to FP16 format when saving OpenVINO model to IR. This option is enabled by default which means all produced IRs are saved using FP16 data type for weights which saves up to 2x storage space for the model file and in most cases doesn’t sacrifice model accuracy. In case it does affect accuracy, the compression can be disabled by setting this flag to False:

import openvino as ov

ov_model = ov.convert_model(original_model)
ov.save_model(ov_model, 'model.xml', compress_to_fp16=False)
ovc path_to_your_model --compress_to_fp16=False

For details on how plugins handle compressed FP16 models, see Inference Devices and Modes.


FP16 compression is sometimes used as the initial step for INT8 quantization. Refer to the Post-training optimization guide for more information about that.

  • extension parameter which makes possible conversion of the models consisting of operations that are not supported by OpenVINO out-of-the-box. It requires implementing of an OpenVINO extension first, please refer to Frontend Extensions guide.

  • share_weigths parameter with default value True allows reusing memory with original weights. For models loaded in Python and then passed to openvino.convert_model, that means that OpenVINO model will share the same areas in program memory where the original weights are located. For models loaded from files by openvino.convert_model, file memory mapping is used to avoid extra memory allocation. When enabled, the original model cannot be modified (Python object cannot be deallocated and original model file cannot be deleted) for the whole lifetime of OpenVINO model. Even model inference by original framework can lead to model modification. If it is not desired, set share_weights=False when calling openvino.convert_model.


    ovc does not have share_weights option and always uses sharing to reduce conversion time and consume less amount of memory during the conversion.

  • output_model parameter in ovc and openvino.save_model specifies name for output .xml file with the resulting OpenVINO IR. The accompanying .bin file name will be generated automatically by replacing .xml extension with .bin extension. The value of output_model must end with .xml extension. For ovc command line tool, output_model can also contain a name of a directory. In this case, the resulting OpenVINO IR files will be put into that directory with a base name of .xml and .bin files matching the original model base name passed to ovc as a parameter. For example, when calling ovc your_model.onnx --output_model directory_name, files directory_name/your_model.xml and directory_name/your_model.bin will be created. If output_model is not used, then the current directory is used as a destination directory.


    openvino.save_model does not support a directory for output_model parameter value because openvino.save_model gets OpenVINO model object represented in a memory and there is no original model file name available for output file name generation. For the same reason, output_model is a mandatory parameter for openvino.save_model.

  • verbose parameter activates extra diagnostics printed to the standard output. Use for debugging purposes in case there is an issue with the conversion and to collect information for better bug reporting to OpenVINO team.


Weights sharing does not equally work for all the supported model formats. The value of this flag is considered as a hint for the conversion API, and actual sharing is used only if it is implemented and possible for a particular model representation.

You can always run ovc -h or ovc --help to recall all the supported parameters for ovc.

Use ovc --version to check the version of OpenVINO package installed.