Feature Extraction
Transformers
Safetensors
qwen2_5_omni
text-to-audio
multimodal
text
image
video
audio
embedding
retrieval
Instructions to use ATH-MaaS/Ovis-Omni-Embedding-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ATH-MaaS/Ovis-Omni-Embedding-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ATH-MaaS/Ovis-Omni-Embedding-3B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ATH-MaaS/Ovis-Omni-Embedding-3B") model = AutoModelForMultimodalLM.from_pretrained("ATH-MaaS/Ovis-Omni-Embedding-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download ovis_embedding_data_centric.png from ATH-MaaS/Ovis-Omni-Embedding-3B: direct link, hf CLI and curl.
- Browser
- Download file 730 kB
-
https://huggingface.co/ATH-MaaS/Ovis-Omni-Embedding-3B/resolve/2c974121ed0d6e0de215d5c834609c857366d55a/ovis_embedding_data_centric.png
- Command line
-
hf download hf://ATH-MaaS/Ovis-Omni-Embedding-3B@2c974121ed0d6e0de215d5c834609c857366d55a/ovis_embedding_data_centric.png
-
curl -L -o ovis_embedding_data_centric.png https://huggingface.co/ATH-MaaS/Ovis-Omni-Embedding-3B/resolve/2c974121ed0d6e0de215d5c834609c857366d55a/ovis_embedding_data_centric.png
730 kB

- Xet hash:
- 5762c9e32abd5717f5bc200e7b7dd0fcbc6298341a4b7f7e72686db3a5e6503d
- Size of remote file:
- 730 kB
- SHA256:
- 0ad28dd7a76521b165d893611d4c5383a041afcb749a65b0cbf47f6ed3e9675f
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