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AtesiT
/
bge-m3-int8-dynamic-quantized

Feature Extraction
sentence-transformers
Russian
xlm-roberta
quantization
int8
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use AtesiT/bge-m3-int8-dynamic-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use AtesiT/bge-m3-int8-dynamic-quantized with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("AtesiT/bge-m3-int8-dynamic-quantized")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
bge-m3-int8-dynamic-quantized
1.38 GB
Ctrl+K
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  • 1 contributor
History: 4 commits
AtesiT's picture
AtesiT
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  • .gitattributes
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  • README.md
    2.64 kB
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  • config.json
    744 Bytes
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  • config_sentence_transformers.json
    284 Bytes
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  • modules.json
    429 Bytes
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  • pytorch_model_quantized.pt
    1.36 GB
    xet
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  • sentence_bert_config.json
    241 Bytes
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  • tokenizer.json
    17.1 MB
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  • tokenizer_config.json
    409 Bytes
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