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‎latest/_sources/autoapi/neural_compressor/adaptor/mxnet_utils/util/index.rst.txt

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.. py:class:: OpType
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.. py:class:: OpType(*args, **kwds)
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‎latest/_sources/autoapi/neural_compressor/adaptor/ox_utils/util/index.rst.txt

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Map data type and its string representation.
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Represents a linearly quantized weight input from ONNX operators.
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.. py:class:: QuantizationMode
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.. py:class:: QuantizationMode(*args, **kwds)
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Represent QuantizationMode value.
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.. py:class:: QuantizedValueType(*args, **kwds)
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Represent QuantizedValueType value.
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.. py:class:: QuantFormat(*args, **kwds)
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‎latest/_sources/autoapi/neural_compressor/adaptor/tf_utils/quantize_graph/quantize_graph_concatv2/index.rst.txt

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.. py:class:: FuseNodeStartWithConcatV2(**kwargs)
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.. py:class:: FuseNodeStartWithConcatV2
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‎latest/_sources/autoapi/neural_compressor/common/tuning_param/index.rst.txt

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:returns: The decorated function.
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A Reg object that defines regulization terms.
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.. py:class:: KerasBasicPruner(config, modules)
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.. py:class:: ONNXRTBertDataLoader(dataset, batch_size=1, last_batch='rollover', collate_fn=None, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, shuffle=False, distributed=False)
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Built-in dataloader for onnx bert model and its variants.
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.. py:class:: ONNXRTDataLoader(dataset, batch_size=1, last_batch='rollover', collate_fn=None, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, shuffle=False, distributed=False)
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:param batch_size: int. batch size
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.. py:class:: TensorflowBertDataLoader(dataset, batch_size=1, last_batch='rollover', collate_fn=None, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, shuffle=False, distributed=False)
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this dataloader is designed to satisfy the usage of Bert models.
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.. py:class:: TensorflowModelZooBertDataLoader(dataset, batch_size=1, last_batch='rollover', collate_fn=None, sampler=None, batch_sampler=None, num_workers=0, pin_memory=False, shuffle=False, distributed=False)
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this dataloader is designed to satisfy the usage of Model Zoo Bert models.
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‎latest/_sources/autoapi/neural_compressor/torch/algorithms/mx_quant/utils/index.rst.txt

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‎latest/_sources/autoapi/neural_compressor/utils/utility/index.rst.txt

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The statistics printer.
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‎latest/_sources/docs/source/publication_list.md.txt

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Full Publications/Events (86)
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Full Publications/Events (87)
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==========
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## 2025 (1)
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* Blog on SqueezeBits: [[Intel Gaudi] #4. FP8 Quantization](https://blog.squeezebits.com/intel-gaudi-4-fp8-quantization--40269) (Jan 2025)
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## 2024 (7)
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* Blog by Microsoft: [Phi-4 quantization and inference speedup](https://techcommunity.microsoft.com/blog/machinelearningblog/phi-4-quantization-and-inference-speedup/4360047) (Dec 2024)
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* EMNLP'2024: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2024)
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* Blog on Medium: [Intel Optimization at Netflix](https://medium.com/@amerather_9719/intel-optimization-at-netflix-79ef0efb9d2) (May 2023)
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* Blog on Medium: [Effective Post-training Quantization for Large Language Models with Enhanced SmoothQuant Approach](https://medium.com/@NeuralCompressor/effective-post-training-quantization-for-large-language-models-with-enhanced-smoothquant-approach-93e9d104fb98) (Apr 2023)
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* Blog by Intel: [Intel® Xeon® Processors Are Still the Only CPU With MLPerf Results, Raising the Bar By 5x](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Intel-Xeon-Processors-Are-Still-the-Only-CPU-With-MLPerf-Results/post/1472750) (Apr 2023)
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* Blog by Intel: [集成英特尔® Neural Compressor，腾讯云TACO Kit为AI应用带来高效异构加速服务]([https://mp.weixin.qq.com/s/I-FQqOuW7HTnwXegLGNAtw](https://www.intel.cn/content/www/cn/zh/customer-spotlight/cases/neural-compressor-tencent-cloud-taco-kit-ai.html)) (Mar 2023)
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* Blog by Intel: [集成英特尔® Neural Compressor，腾讯云TACO Kit为AI应用带来高效异构加速服务](https://www.intel.cn/content/www/cn/zh/customer-spotlight/cases/neural-compressor-tencent-cloud-taco-kit-ai.html) (Mar 2023)
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* Post on Social Media: [Adopt with Tencent TACO: Heterogeneous optimization is also key to improving AI computing power](https://mp.weixin.qq.com/s/I-FQqOuW7HTnwXegLGNAtw) (Mar 2023)
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* Blog on Medium: [Structured Pruning for Transformer-Based Models](https://medium.com/intel-analytics-software/structured-pruning-for-transformer-based-models-116e949ef12c) (Jan 2023)
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* Post on Social Media: [Training and Inference for Stable Diffusion | Intel Business](https://www.youtube.com/watch?v=emCgSTlJaAg) (Jan 2023)

‎latest/autoapi/neural_compressor/adaptor/mxnet_utils/index.html

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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">neural_compressor.adaptor.mxnet_utils.util.</span></span><span class="sig-name descname"><span class="pre">OpType</span></span><a class="reference external" href="https://github.com/intel/neural-compressor/blob/master/neural_compressor/adaptor/mxnet_utils/util.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#neural_compressor.adaptor.mxnet_utils.util.OpType" title="Link to this definition"></a></dt>
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">neural_compressor.adaptor.mxnet_utils.util.</span></span><span class="sig-name descname"><span class="pre">OpType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwds</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/intel/neural-compressor/blob/master/neural_compressor/adaptor/mxnet_utils/util.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#neural_compressor.adaptor.mxnet_utils.util.OpType" title="Link to this definition"></a></dt>
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