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README.md
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base_model: Qwen/Qwen3-Reranker-0.6B
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library_name: model2vec
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license: mit
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model_name:
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tags:
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- embeddings
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- static-embeddings
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- sentence-transformers
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---
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#
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This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the Qwen/Qwen3-Reranker-0.6B(https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) 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.
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from model2vec import StaticModel
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# Load a pretrained Model2Vec model
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model = StaticModel.from_pretrained("
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# Compute text embeddings
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embeddings = model.encode(["Example sentence"])
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from sentence_transformers import SentenceTransformer
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# Load a pretrained Sentence Transformer model
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model = SentenceTransformer("
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# Compute text embeddings
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embeddings = model.encode(["Example sentence"])
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base_model: Qwen/Qwen3-Reranker-0.6B
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library_name: model2vec
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license: mit
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model_name: m2v_model1
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tags:
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- embeddings
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- static-embeddings
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- sentence-transformers
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---
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# m2v_model1 Model Card
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This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the Qwen/Qwen3-Reranker-0.6B(https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) 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.
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from model2vec import StaticModel
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# Load a pretrained Model2Vec model
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model = StaticModel.from_pretrained("m2v_model1")
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# Compute text embeddings
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embeddings = model.encode(["Example sentence"])
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from sentence_transformers import SentenceTransformer
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# Load a pretrained Sentence Transformer model
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model = SentenceTransformer("m2v_model1")
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# Compute text embeddings
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embeddings = model.encode(["Example sentence"])
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