Feature Extraction
Transformers
Safetensors
qwen3_5
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, Qwen3_5ForEmbedding processor = AutoProcessor.from_pretrained("Alibaba-NLP/UEmbed-9B") model = Qwen3_5ForEmbedding.from_pretrained("Alibaba-NLP/UEmbed-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 484ce6782d5417f89ec37699cebc952d1448bc74725a956cf4b3b9d537d1f278
- Size of remote file:
- 1.51 GB
- SHA256:
- 9db07fe0cba521ea1ab35d1fa7a727b5972444e50d5fd5a771907090a84644e4
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