Feature Extraction
Transformers
Safetensors
English
Korean
multilingual
qwen3_5
vision-language
embedding
multimodal-embedding
mmeb
custom_code
Instructions to use Urock-AI/eddy-vl_embedding_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Urock-AI/eddy-vl_embedding_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Urock-AI/eddy-vl_embedding_v2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Urock-AI/eddy-vl_embedding_v2", trust_remote_code=True) model = AutoModel.from_pretrained("Urock-AI/eddy-vl_embedding_v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from Urock-AI/eddy-vl_embedding_v2: direct link, hf CLI and curl.
- Browser
- Download file 113 Bytes
-
https://huggingface.co/Urock-AI/eddy-vl_embedding_v2/resolve/main/modules.json
- Command line
-
hf download hf://Urock-AI/eddy-vl_embedding_v2/modules.json
-
curl -L -o modules.json https://huggingface.co/Urock-AI/eddy-vl_embedding_v2/resolve/main/modules.json
113 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "eddy_vl_embedding_v2_st.WeMMTransformer" | |
| } | |
| ] | |