Feature Extraction
Transformers
Safetensors
vila
omni-modal
multimodal
vision
audio
video
llm
custom_code
Eval Results (legacy)
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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Use transformers as the library name
Browse files
README.md
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license: other
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---
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# <span style="background: linear-gradient(45deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #f5576c 75%, #4facfe 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; font-weight: bold; font-size: 1.1em;">**OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM**</span> <br />
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journal={arXiv},
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year={2025},
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}
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```
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license: other
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library_name: transformers
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# <span style="background: linear-gradient(45deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #f5576c 75%, #4facfe 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; font-weight: bold; font-size: 1.1em;">**OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM**</span> <br />
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journal={arXiv},
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year={2025},
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}
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```
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