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RedHatAI
/
bge-large-en-v1.5-quant

Feature Extraction
Transformers
ONNX
English
bert
sparse
sparsity
quantized
embeddings
int8
mteb
deepsparse
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use RedHatAI/bge-large-en-v1.5-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use RedHatAI/bge-large-en-v1.5-quant with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="RedHatAI/bge-large-en-v1.5-quant")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("RedHatAI/bge-large-en-v1.5-quant")
    model = AutoModel.from_pretrained("RedHatAI/bge-large-en-v1.5-quant")
  • Notebooks
  • Google Colab
  • Kaggle
bge-large-en-v1.5-quant
432 MB
Ctrl+K
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  • 1 contributor
History: 23 commits
zeroshot's picture
zeroshot
Update README.md
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  • .gitattributes
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    initial commit over 2 years ago
  • README.md
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  • bge-large-latency.png
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  • config.json
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  • model.onnx
    431 MB
    xet
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  • tokenizer.json
    711 kB
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