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PXY107kWqS
/
Qwen3-Reranker-8B-bnb-4bit

Text Ranking
Transformers
Safetensors
sentence-transformers
qwen3
feature-extraction
bnb-my-repo
4-bit precision
bitsandbytes
Model card Files Files and versions
xet
Community

Instructions to use PXY107kWqS/Qwen3-Reranker-8B-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use PXY107kWqS/Qwen3-Reranker-8B-bnb-4bit with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("PXY107kWqS/Qwen3-Reranker-8B-bnb-4bit")
    model = AutoModel.from_pretrained("PXY107kWqS/Qwen3-Reranker-8B-bnb-4bit")
  • sentence-transformers

    How to use PXY107kWqS/Qwen3-Reranker-8B-bnb-4bit with sentence-transformers:

    from sentence_transformers import CrossEncoder
    
    model = CrossEncoder("PXY107kWqS/Qwen3-Reranker-8B-bnb-4bit")
    
    query = "Which planet is known as the Red Planet?"
    passages = [
    	"Venus is often called Earth's twin because of its similar size and proximity.",
    	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
    	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
    ]
    
    scores = model.predict([(query, passage) for passage in passages])
    print(scores)
  • Notebooks
  • Google Colab
  • Kaggle
Qwen3-Reranker-8B-bnb-4bit
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  • 1 contributor
History: 2 commits
PXY107kWqS's picture
PXY107kWqS
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  • .gitattributes
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  • README.md
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  • added_tokens.json
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  • chat_template.jinja
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  • config.json
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  • merges.txt
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  • model-00001-of-00002.safetensors
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  • model-00002-of-00002.safetensors
    165 MB
    xet
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  • model.safetensors.index.json
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  • special_tokens_map.json
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  • tokenizer.json
    11.4 MB
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  • tokenizer_config.json
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  • vocab.json
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