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sofieneb
/
omni-base

Image Feature Extraction
Transformers
Safetensors
omni
feature-extraction
histopathology
digital-pathology
foundation-model
vision-transformer
mixture-of-experts
knowledge-distillation
custom_code
Model card Files Files and versions
xet
Community

Instructions to use sofieneb/omni-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use sofieneb/omni-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-feature-extraction", model="sofieneb/omni-base", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("sofieneb/omni-base", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
omni-base
456 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
sofieneb's picture
sofieneb
Update README.md
e926168 verified about 8 hours ago
  • .gitattributes
    1.52 kB
    OMNI-base: Scaling Multi-Teacher Distillation for Digital Pathology about 8 hours ago
  • README.md
    6.19 kB
    Update README.md about 8 hours ago
  • config.json
    684 Bytes
    OMNI-base: Scaling Multi-Teacher Distillation for Digital Pathology about 8 hours ago
  • eval_thunder.py
    5.72 kB
    OMNI-base: Scaling Multi-Teacher Distillation for Digital Pathology about 8 hours ago
  • model.safetensors
    456 MB
    xet
    OMNI-base: Scaling Multi-Teacher Distillation for Digital Pathology about 8 hours ago
  • modeling_omni.py
    14.9 kB
    OMNI-base: Scaling Multi-Teacher Distillation for Digital Pathology about 8 hours ago
  • requirements.txt
    90 Bytes
    OMNI-base: Scaling Multi-Teacher Distillation for Digital Pathology about 8 hours ago