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tencent
/
Youtu-HiChunk

Feature Extraction
Transformers
Safetensors
Chinese
utu_v1
custom_code
Model card Files Files and versions
xet
Community

Instructions to use tencent/Youtu-HiChunk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tencent/Youtu-HiChunk with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="tencent/Youtu-HiChunk", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("tencent/Youtu-HiChunk", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Youtu-HiChunk
8.17 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 3 commits
Luuuuk's picture
Luuuuk
Upload logo.png
a0a9d50 verified 3 months ago
  • assets
    Upload logo.png 3 months ago
  • .gitattributes
    1.79 kB
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  • HiChunk.py
    16.2 kB
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  • LICENSE.txt
    12.1 kB
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  • README.md
    3.52 kB
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  • config.json
    1.41 kB
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  • configuration_utu_v1.py
    12.6 kB
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  • generation_config.json
    143 Bytes
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  • model-00001-of-00002.safetensors
    4.99 GB
    xet
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  • model-00002-of-00002.safetensors
    3.18 GB
    xet
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  • model.safetensors.index.json
    37 kB
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  • modeling_utu_v1.py
    52.2 kB
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  • registry.py
    26.8 kB
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  • special_tokens_map.json
    449 Bytes
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  • tokenizer.json
    9.35 MB
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  • tokenizer_config.json
    53.5 kB
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  • utu_v1.py
    35.6 kB
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  • zero_to_fp32.py
    33.3 kB
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