Feature Extraction
Transformers
Safetensors
qwen3_5
image-text-to-text
multimodal
text
image
video
visual-document
embedding
retrieval
Instructions to use ATH-MaaS/Ovis-VL-Embedding-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ATH-MaaS/Ovis-VL-Embedding-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ATH-MaaS/Ovis-VL-Embedding-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ATH-MaaS/Ovis-VL-Embedding-9B") model = AutoModelForMultimodalLM.from_pretrained("ATH-MaaS/Ovis-VL-Embedding-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Ovis-VL-Embedding-9B
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**Ovis-VL-Embedding-9B** is a high-capacity vision-language embedding model for text, images, visual documents, video, and interleaved multimodal inputs. It maps every supported input type into one coherent representation space, enabling high-accuracy cross-modal retrieval with a single encoder.
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# Ovis-VL-Embedding-9B
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<img src=ovis_logo.png width="30%"/>
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**Ovis-VL-Embedding-9B** is a high-capacity vision-language embedding model for text, images, visual documents, video, and interleaved multimodal inputs. It maps every supported input type into one coherent representation space, enabling high-accuracy cross-modal retrieval with a single encoder.
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