Feature Extraction
Transformers
Safetensors
English
Korean
multilingual
qwen3_vl
vision-language
embedding
multimodal-embedding
mmeb
digital-forensics
custom_code
Instructions to use Urock-AI/Eddy-vl_embedding_1.9B_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Urock-AI/Eddy-vl_embedding_1.9B_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Urock-AI/Eddy-vl_embedding_1.9B_v1", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Urock-AI/Eddy-vl_embedding_1.9B_v1", trust_remote_code=True) model = AutoModel.from_pretrained("Urock-AI/Eddy-vl_embedding_1.9B_v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf68b624c2b3c64a4c1454d0ff75ad2e1341a15d8d511bf4a6424d95d6a5350c
- Size of remote file:
- 11.4 MB
- SHA256:
- db74fda9dc5dbe348cb79bda440c969b3ac982a78358d8d884f64ce92098b104
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