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")# pip install -U transformers accelerate # 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
Download configuration.json from Alibaba-NLP/UEmbed-9B: direct link, hf CLI and curl.
- Browser
- Download file 51 Bytes
-
https://huggingface.co/Alibaba-NLP/UEmbed-9B/resolve/main/configuration.json
- Command line
-
hf download hf://Alibaba-NLP/UEmbed-9B/configuration.json
-
curl -L -o configuration.json https://huggingface.co/Alibaba-NLP/UEmbed-9B/resolve/main/configuration.json
51 Bytes
| {"framework":"Pytorch","task":"image-text-to-text"} |