cnmoro
/

Linq-Embed-Mistral-Distilled

sentence-transformers

static-embeddings

8-bit precision

Model card Files Files and versions

cnmoro commited on May 14, 2025

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 - sentence-transformers
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-# distilled-linq-mistral-i8 Model Card
 This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the Linq-AI-Research/Linq-Embed-Mistral(https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.

 - sentence-transformers
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+# Linq-Embed-Mistral-Distilled Model Card
 This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the Linq-AI-Research/Linq-Embed-Mistral(https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.