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 figures/ovis_blog_table2.pdf from ATH-MaaS/Ovis-VL-Embedding-9B: direct link, hf CLI and curl.
- Browser
- Download file 482 kB
-
https://huggingface.co/ATH-MaaS/Ovis-VL-Embedding-9B/resolve/dc11d278686a40d5b9516d6de21e7ad155ccf80a/figures/ovis_blog_table2.pdf
- Command line
-
hf download hf://ATH-MaaS/Ovis-VL-Embedding-9B@dc11d278686a40d5b9516d6de21e7ad155ccf80a/figures/ovis_blog_table2.pdf
-
curl -L -o ovis_blog_table2.pdf https://huggingface.co/ATH-MaaS/Ovis-VL-Embedding-9B/resolve/dc11d278686a40d5b9516d6de21e7ad155ccf80a/figures/ovis_blog_table2.pdf
482 kB
- Xet hash:
- f49ec95eefc299fa8377ba02ecc3ec1302b0e49e649c10a6c32a3eeae922dceb
- Size of remote file:
- 482 kB
- SHA256:
- 1cb316f64ae7b08db5b8c95e4f5ce5ab0b196d6fb3684df2629216d4b4f52aef
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