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
Update README.md
Browse files
README.md
CHANGED
|
@@ -108,6 +108,20 @@ The model is intended for embedding extraction and retrieval over supported unim
|
|
| 108 |
- The 4096-dimensional output and 9B-scale backbone require more memory, storage, and inference compute than the 2B variant.
|
| 109 |
- Benchmark scores may not directly predict performance on a new domain. Evaluate with representative queries, candidates, and retrieval metrics before deployment.
|
| 110 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 111 |
## License
|
| 112 |
|
| 113 |
This model is released under the Apache 2.0 license.
|
|
|
|
| 108 |
- The 4096-dimensional output and 9B-scale backbone require more memory, storage, and inference compute than the 2B variant.
|
| 109 |
- Benchmark scores may not directly predict performance on a new domain. Evaluate with representative queries, candidates, and retrieval metrics before deployment.
|
| 110 |
|
| 111 |
+
## Citation
|
| 112 |
+
|
| 113 |
+
If you find our embedding models useful, please consider citing our technical report:
|
| 114 |
+
|
| 115 |
+
```bibtex
|
| 116 |
+
@article{ovisembedding2026,
|
| 117 |
+
title = {Ovis-Embedding: Pushing the Frontiers of Universal Omni-Modal Embeddings},
|
| 118 |
+
author = {{Ovis-Embedding Team}},
|
| 119 |
+
journal = {arXiv preprint arXiv:2609.25165},
|
| 120 |
+
year = {2026},
|
| 121 |
+
url = {https://arxiv.org/abs/2609.25165}
|
| 122 |
+
}
|
| 123 |
+
```
|
| 124 |
+
|
| 125 |
## License
|
| 126 |
|
| 127 |
This model is released under the Apache 2.0 license.
|