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omai-research
/
milco-650m

Feature Extraction
Transformers
PyTorch
milco
sparse-retrieval
multilingual
learned-sparse
cross-lingual
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use omai-research/milco-650m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use omai-research/milco-650m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="omai-research/milco-650m", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("omai-research/milco-650m", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
milco-650m
2.39 GB
Ctrl+K
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  • 1 contributor
History: 6 commits
thongnt's picture
thongnt
Update README.md
567aeb0 verified 4 months ago
  • .gitattributes
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  • README.md
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  • config.json
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  • milco.py
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict"

    What is a pickle import?

    2.37 GB
    xet
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  • sentencepiece.bpe.model
    5.07 MB
    xet
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    1.2 kB
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