Zero-Shot Image Classification
Transformers
Safetensors
English
qwen2_5_vl
mmeb
text-generation-inference
Instructions to use moca-embed/MoCa-Qwen25VL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moca-embed/MoCa-Qwen25VL-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="moca-embed/MoCa-Qwen25VL-7B") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, Qwen2_5ForEmbedding processor = AutoProcessor.from_pretrained("moca-embed/MoCa-Qwen25VL-7B") model = Qwen2_5ForEmbedding.from_pretrained("moca-embed/MoCa-Qwen25VL-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update pipeline tag and fix paper link in model card
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Updating the
pipeline_tagfromzero-shot-image-classificationtofeature-extraction. This better reflects the model's primary function as a multimodal embedding model and helps users discover it under the correct category on the Hub (https://huggingface.co/models?pipeline_tag=feature-extraction). - Correcting the placeholder paper link in the content to the official arXiv link: https://arxiv.org/abs/2506.23115.