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@@ -81,6 +81,15 @@ In addition, Intel® Extension for PyTorch* introduces module level optimization
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The feature provides optimized alternatives for several commonly used LLM modules and functionalities for the optimizations of the niche or customized LLMs.
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Please read [**LLM module level optimization practice**](./examples/cpu/inference/python/llm-modeling) to better understand how to optimize your own LLM and achieve better performance.
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Above models are intended to allow users to examine and evaluate models and the associated performance of Intel technology solutions.
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The accuracy of computer models is a function of the relation between the data used to train them and the data that the models encounter after deployment.
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Models have been tested using datasets that may or may not be sufficient for use in production applications.
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Accordingly, while the model may serve as a strong foundation, Intel recommends and requests that those models be tested against data the models are likely to encounter in specific deployments.
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Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights.
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See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf).
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Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
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## Support
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The team tracks bugs and enhancement requests using [GitHub issues](https://github.com/intel/intel-extension-for-pytorch/issues/). Before submitting a suggestion or bug report, search the existing GitHub issues to see if your issue has already been reported.
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