Feature Extraction
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
embedding
multimodal
Instructions to use naver-ai/MuCo-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver-ai/MuCo-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver-ai/MuCo-2B")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("naver-ai/MuCo-2B") model = AutoModelForImageTextToText.from_pretrained("naver-ai/MuCo-2B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e60afffd20fb9c177b7f515cc2a54e2de0905586c370b71710dc58790510ca9
- Size of remote file:
- 11.4 MB
- SHA256:
- 662e94ef50a214a73c0cb319af42d5fc5cc554d3e6814e91cdbe8b3f86aeb1da
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