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### Description Support SmoothQuant for ORT static quantization via intel neural compressor > Note: Please use neural-compressor==2.2 to try SmoothQuant function. ### Motivation and Context For large language models (LLMs) with gigantic parameters, the systematic outliers make quantification of activations difficult. As a training free post-training quantization (PTQ) solution, SmoothQuant offline migrates this difficulty from activations to weights with a mathematically equivalent transformation. Integrating SmoothQuant into ORT quantization can benefit the accuracy of INT8 LLMs. --------- Signed-off-by: Mengni Wang <mengni.wang@intel.com> |
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| contrib_ops | ||
| core | ||
| python | ||
| test | ||
| tool/etw | ||
| wasm | ||
| __init__.py | ||
| ReformatSource.ps1 | ||
| ReformatSourcePython.bat | ||
| VSCodeCoverage.runsettings | ||