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zenlm
/
zen3-embedding

Feature Extraction
Transformers
Safetensors
qwen3
text-generation
embedding
retrieval
zen
zen3
hanzo
zenlm
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use zenlm/zen3-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use zenlm/zen3-embedding with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="zenlm/zen3-embedding")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("zenlm/zen3-embedding")
    model = AutoModelForCausalLM.from_pretrained("zenlm/zen3-embedding")
  • Notebooks
  • Google Colab
  • Kaggle
zen3-embedding
8.06 GB
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  • 1 contributor
History: 5 commits
zeekay's picture
zeekay
Update model card: fix YAML tags, add zen/zenlm branding
040df8e verified 2 months ago
  • 1_Pooling
    Add model weights 2 months ago
  • .gitattributes
    1.57 kB
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  • README.md
    935 Bytes
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  • config.json
    727 Bytes
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  • config_sentence_transformers.json
    215 Bytes
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  • generation_config.json
    117 Bytes
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  • merges.txt
    1.67 MB
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  • model-00001-of-00002.safetensors
    4.97 GB
    xet
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  • model-00002-of-00002.safetensors
    3.08 GB
    xet
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  • model.safetensors.index.json
    30.4 kB
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  • modules.json
    349 Bytes
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  • tokenizer.json
    11.4 MB
    xet
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  • tokenizer_config.json
    7.26 kB
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  • vocab.json
    2.78 MB
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