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-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/UEmbed-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Alibaba-NLP/UEmbed-4B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Alibaba-NLP/UEmbed-4B") model = AutoModelForMultimodalLM.from_pretrained("Alibaba-NLP/UEmbed-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sparse_info.json from Alibaba-NLP/UEmbed-4B: direct link, hf CLI and curl.
- Browser
- Download file 5.75 MB
-
https://huggingface.co/Alibaba-NLP/UEmbed-4B/resolve/main/sparse_info.json
- Command line
-
hf download hf://Alibaba-NLP/UEmbed-4B/sparse_info.json
-
curl -L -o sparse_info.json https://huggingface.co/Alibaba-NLP/UEmbed-4B/resolve/main/sparse_info.json
5.75 MB
File too large to display, you can check the raw version instead.