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# 🚀 Check out [our new v3-small model](https://huggingface.co/redis/langcache-embed-v3-small), trained for improved inference speed, lighter footprint, and better semantic matching for caching.
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## Redis semantic caching embedding model based on Alibaba-NLP/gte-modernbert-base
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching.
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# 🚀 Check out [our new v3-small model](https://huggingface.co/redis/langcache-embed-v3-small), trained for improved inference speed, lighter footprint, and better semantic matching for caching.
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## Redis semantic caching embedding model based on Alibaba-NLP/gte-modernbert-base
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching.
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