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Teradata
/
qwen3-embedding-0.6b

Feature Extraction
Transformers
ONNX
multilingual
qwen3
text-generation
teradata
byom
embeddings
qwen
decoder
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Teradata/qwen3-embedding-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Teradata/qwen3-embedding-0.6b with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Teradata/qwen3-embedding-0.6b")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Teradata/qwen3-embedding-0.6b")
    model = AutoModelForCausalLM.from_pretrained("Teradata/qwen3-embedding-0.6b")
  • Notebooks
  • Google Colab
  • Kaggle
qwen3-embedding-0.6b
1.4 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
sasha-smirnov's picture
sasha-smirnov
Initial publish via td-embeddings
c3350e0 verified 1 day ago
  • onnx
    Initial publish via td-embeddings 1 day ago
  • .gitattributes
    1.57 kB
    Initial publish via td-embeddings 1 day ago
  • README.md
    9.23 kB
    Initial publish via td-embeddings 1 day ago
  • config.json
    727 Bytes
    Initial publish via td-embeddings 1 day ago
  • generation_config.json
    117 Bytes
    Initial publish via td-embeddings 1 day ago
  • tokenizer.json
    11.4 MB
    xet
    Initial publish via td-embeddings 1 day ago
  • tokenizer_config.json
    9.71 kB
    Initial publish via td-embeddings 1 day ago