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
Download ovis_embedding_model_architecture.png from ATH-MaaS/Ovis-VL-Embedding-9B: direct link, hf CLI and curl.
- Browser
- Download file 401 kB
-
https://huggingface.co/ATH-MaaS/Ovis-VL-Embedding-9B/resolve/85a53a7daf68d30bfea8f30617f2132a92868a16/ovis_embedding_model_architecture.png
- Command line
-
hf download hf://ATH-MaaS/Ovis-VL-Embedding-9B@85a53a7daf68d30bfea8f30617f2132a92868a16/ovis_embedding_model_architecture.png
-
curl -L -o ovis_embedding_model_architecture.png https://huggingface.co/ATH-MaaS/Ovis-VL-Embedding-9B/resolve/85a53a7daf68d30bfea8f30617f2132a92868a16/ovis_embedding_model_architecture.png
401 kB

- Xet hash:
- 3b1ebf87f3f15bee7ff56d55307546b2da255115d4b32fa76f5eebd81569745e
- Size of remote file:
- 401 kB
- SHA256:
- da256408d2995ba6883bdd352b2c5c62c00769f0a6f614cfb1905438578c1e4a
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