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nvidia
/
omnivinci

Feature Extraction
Transformers
Safetensors
vila
omni-modal
multimodal
vision
audio
video
llm
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community
7

Instructions to use nvidia/omnivinci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nvidia/omnivinci with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="nvidia/omnivinci", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("nvidia/omnivinci", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
omnivinci / llm
15.2 GB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 1 commit
Hanrong Ye
commit
c48c32c 4 months ago
  • added_tokens.json
    455 Bytes
    commit 4 months ago
  • config.json
    186 kB
    commit 4 months ago
  • generation_config.json
    243 Bytes
    commit 4 months ago
  • merges.txt
    1.67 MB
    commit 4 months ago
  • model-00001-of-00004.safetensors
    4.87 GB
    xet
    commit 4 months ago
  • model-00002-of-00004.safetensors
    4.93 GB
    xet
    commit 4 months ago
  • model-00003-of-00004.safetensors
    4.33 GB
    xet
    commit 4 months ago
  • model-00004-of-00004.safetensors
    1.09 GB
    xet
    commit 4 months ago
  • model.safetensors.index.json
    27.8 kB
    commit 4 months ago
  • special_tokens_map.json
    753 Bytes
    commit 4 months ago
  • tokenizer.json
    11.4 MB
    xet
    commit 4 months ago
  • tokenizer_config.json
    4.04 kB
    commit 4 months ago
  • vocab.json
    2.78 MB
    commit 4 months ago