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  This repository implements a `custom` task for `sentence-embeddings` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optimum](https://huggingface.co/docs/optimum/index). The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/all-MiniLM-L6-v2-optimum-embeddings/blob/main/pipeline.py).
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- Below is also describe how we converted & optimized the model, based on the [Accelerate Sentence Transformers with Hugging Face Optimum](https://www.philschmid.de/optimize-sentence-transformers) blog post. You can also check out the [notebook](https://huggingface.co/philschmid/all-MiniLM-L6-v2-optimum-embeddings/blob/main/convert.ipynb).
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  To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `pipeline.py` file. -> _double check if it is selected_
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  This repository implements a `custom` task for `sentence-embeddings` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optimum](https://huggingface.co/docs/optimum/index). The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/all-MiniLM-L6-v2-optimum-embeddings/blob/main/pipeline.py).
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+ In the [how to create your own optimized and quantized model](#how-to-create-your-own-optimized-and-quantized-model) you will learn how the model was converted & optimized, it is based on the [Accelerate Sentence Transformers with Hugging Face Optimum](https://www.philschmid.de/optimize-sentence-transformers) blog post. It also includes how to create your custom pipeline and test it. There is also a [notebook](https://huggingface.co/philschmid/all-MiniLM-L6-v2-optimum-embeddings/blob/main/convert.ipynb) included.
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  To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `pipeline.py` file. -> _double check if it is selected_
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