Image Feature Extraction
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
mmeb
multimodal
reasoning
text-generation-inference
Instructions to use MCG-NJU/RGE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MCG-NJU/RGE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="MCG-NJU/RGE")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("MCG-NJU/RGE") model = AutoModelForMultimodalLM.from_pretrained("MCG-NJU/RGE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7154deb361b98a58f8ecee455a4a7f2d2a99e796d1c2d14739bde789a5b7074f
- Size of remote file:
- 7.67 kB
- SHA256:
- 0103e8a8049bb16d72ca6e47fad97ddd274fb42c8e7eb31b4792c9ef71b89b0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.