Feature Extraction
Transformers
Safetensors
qwen3_5
image-text-to-text
embeddings
multimodal
retrieval
sparse-retrieval
splade
dense-retrieval
vision
Instructions to use Alibaba-NLP/UEmbed-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/UEmbed-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Alibaba-NLP/UEmbed-9B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Alibaba-NLP/UEmbed-9B") model = AutoModelForMultimodalLM.from_pretrained("Alibaba-NLP/UEmbed-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c829611c0dc7e06c5d7c845dcf584bc609795f1e29ccac997afff57b4672cdc3
- Size of remote file:
- 20 MB
- SHA256:
- 88c9e5503a3d642304938a43cd07c3cd2e3e3bd75493dea4037ef39b85686c3b
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