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:
- 4502ccf2789c6c736efabb35ea0990674d96fa0c7ec5343857b64101b72a46c4
- Size of remote file:
- 3.85 GB
- SHA256:
- 0358da2c13820a18ecbd6d7b6b55d1fe9922abb4b657b8f4da26da56f44f417a
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