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  # Optimized and Quantized [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) with a custom pipeline.py
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- This repository implements a `custom` task for `sentence-embeddings` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optiumum](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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  # Optimized and Quantized [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) with a custom pipeline.py
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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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