# Reference `optimum.intel.neural_compressor` is deprecated and will be removed in the next major release. ## INCQuantizer[[optimum.intel.INCQuantizer]] #### optimum.intel.INCQuantizer[[optimum.intel.INCQuantizer]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/quantization.py#L78) Handle the Neural Compressor quantization process. get_calibration_datasetoptimum.intel.INCQuantizer.get_calibration_datasethttps://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/quantization.py#L247[{"name": "dataset_name", "val": ": str"}, {"name": "num_samples", "val": ": int = 100"}, {"name": "dataset_config_name", "val": ": typing.Optional[str] = None"}, {"name": "dataset_split", "val": ": str = 'train'"}, {"name": "preprocess_function", "val": ": typing.Optional[typing.Callable] = None"}, {"name": "preprocess_batch", "val": ": bool = True"}, {"name": "use_auth_token", "val": ": typing.Union[bool, str, NoneType] = None"}, {"name": "token", "val": ": typing.Union[bool, str, NoneType] = None"}]- **dataset_name** (`str`) -- The dataset repository name on the Hugging Face Hub or path to a local directory containing data files in generic formats and optionally a dataset script, if it requires some code to read the data files. - **num_samples** (`int`, defaults to 100) -- The maximum number of samples composing the calibration dataset. - **dataset_config_name** (`str`, *optional*) -- The name of the dataset configuration. - **dataset_split** (`str`, defaults to `"train"`) -- Which split of the dataset to use to perform the calibration step. - **preprocess_function** (`Callable`, *optional*) -- Processing function to apply to each example after loading dataset. - **preprocess_batch** (`bool`, defaults to `True`) -- Whether the `preprocess_function` should be batched. - **use_auth_token** (Optional[Union[bool, str]], defaults to `None`) -- Deprecated. Please use `token` instead. - **token** (Optional[Union[bool, str]], defaults to `None`) -- The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).0The calibration `datasets.Dataset` to use for the post-training static quantization calibration step. Create the calibration `datasets.Dataset` to use for the post-training static quantization calibration step. **Parameters:** dataset_name (`str`) : The dataset repository name on the Hugging Face Hub or path to a local directory containing data files in generic formats and optionally a dataset script, if it requires some code to read the data files. num_samples (`int`, defaults to 100) : The maximum number of samples composing the calibration dataset. dataset_config_name (`str`, *optional*) : The name of the dataset configuration. dataset_split (`str`, defaults to `"train"`) : Which split of the dataset to use to perform the calibration step. preprocess_function (`Callable`, *optional*) : Processing function to apply to each example after loading dataset. preprocess_batch (`bool`, defaults to `True`) : Whether the `preprocess_function` should be batched. use_auth_token (Optional[Union[bool, str]], defaults to `None`) : Deprecated. Please use `token` instead. token (Optional[Union[bool, str]], defaults to `None`) : The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). **Returns:** The calibration `datasets.Dataset` to use for the post-training static quantization calibration step. #### quantize[[optimum.intel.INCQuantizer.quantize]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/quantization.py#L120) Quantize a model given the optimization specifications defined in `quantization_config`. **Parameters:** quantization_config (`Union[PostTrainingQuantConfig]`) : The configuration containing the parameters related to quantization. save_directory (`Union[str, Path]`) : The directory where the quantized model should be saved. calibration_dataset (`datasets.Dataset`, defaults to `None`) : The dataset to use for the calibration step, needed for post-training static quantization. batch_size (`int`, defaults to 8) : The number of calibration samples to load per batch. data_collator (`DataCollator`, defaults to `None`) : The function to use to form a batch from a list of elements of the calibration dataset. remove_unused_columns (`bool`, defaults to `True`) : Whether or not to remove the columns unused by the model forward method. ## INCTrainer[[optimum.intel.INCTrainer]] #### optimum.intel.INCTrainer[[optimum.intel.INCTrainer]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/trainer.py#L109) INCTrainer enables Intel Neural Compression quantization aware training, pruning and distillation. compute_distillation_lossoptimum.intel.INCTrainer.compute_distillation_losshttps://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/trainer.py#L843[{"name": "student_outputs", "val": ""}, {"name": "teacher_outputs", "val": ""}] How the distillation loss is computed given the student and teacher outputs. #### compute_loss[[optimum.intel.INCTrainer.compute_loss]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/trainer.py#L767) How the loss is computed by Trainer. By default, all models return the loss in the first element. #### save_model[[optimum.intel.INCTrainer.save_model]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/trainer.py#L676) Will save the model, so you can reload it using `from_pretrained()`. Will only save from the main process. ## INCModel[[optimum.intel.INCModel]] #### optimum.intel.INCModel[[optimum.intel.INCModel]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L71) ## INCModelForSequenceClassification[[optimum.intel.INCModelForSequenceClassification]] #### optimum.intel.INCModelForSequenceClassification[[optimum.intel.INCModelForSequenceClassification]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L396) ## INCModelForQuestionAnswering[[optimum.intel.INCModelForQuestionAnswering]] #### optimum.intel.INCModelForQuestionAnswering[[optimum.intel.INCModelForQuestionAnswering]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L391) ## INCModelForTokenClassification[[optimum.intel.INCModelForTokenClassification]] #### optimum.intel.INCModelForTokenClassification[[optimum.intel.INCModelForTokenClassification]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L401) ## INCModelForMultipleChoice[[optimum.intel.INCModelForMultipleChoice]] #### optimum.intel.INCModelForMultipleChoice[[optimum.intel.INCModelForMultipleChoice]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L406) ## INCModelForMaskedLM[[optimum.intel.INCModelForMaskedLM]] #### optimum.intel.INCModelForMaskedLM[[optimum.intel.INCModelForMaskedLM]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L416) ## INCModelForCausalLM[[optimum.intel.INCModelForCausalLM]] #### optimum.intel.INCModelForCausalLM[[optimum.intel.INCModelForCausalLM]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L426) ## INCModelForSeq2SeqLM[[optimum.intel.INCModelForSeq2SeqLM]] #### optimum.intel.INCModelForSeq2SeqLM[[optimum.intel.INCModelForSeq2SeqLM]] [Source](https://github.com/huggingface/optimum-intel/blob/main/optimum/intel/neural_compressor/modeling_base.py#L411)