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:
- bbfefaa4b8db655c848432b22ebc43988e50fe84eff7be3dd7db45bb7237ea8f
- Size of remote file:
- 20 MB
- SHA256:
- 87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4
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