Feature Extraction
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
multimodal
video embedding
ncsoft
ncai
varco
Instructions to use NCSOFT/GME-VARCO-VISION-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NCSOFT/GME-VARCO-VISION-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="NCSOFT/GME-VARCO-VISION-Embedding")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("NCSOFT/GME-VARCO-VISION-Embedding") model = AutoModelForMultimodalLM.from_pretrained("NCSOFT/GME-VARCO-VISION-Embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f8cb22719a1e255779e4a5d6be694efd9aa937523acd62fb0af4dd5336e9036
- Size of remote file:
- 7.99 GB
- SHA256:
- 542a49a9d07a108f5ec22c8701f5f1e7c4dc640f56dd35a2f992331caff9b2ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.