Feature Extraction
Transformers
PyTorch
qwen3_vl
embeddings
multimodal
retrieval
compositional-reasoning
vision
reranker-distillation
Instructions to use Alibaba-NLP/core-emb-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alibaba-NLP/core-emb-2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Alibaba-NLP/core-emb-2b")# Load model directly from transformers import AutoProcessor, Qwen3VLForEmbedding processor = AutoProcessor.from_pretrained("Alibaba-NLP/core-emb-2b") model = Qwen3VLForEmbedding.from_pretrained("Alibaba-NLP/core-emb-2b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e46919b01a4c8bc879959d9f15548729c477484208c1ac6afe8f287a789b8f9
- Size of remote file:
- 3.52 GB
- SHA256:
- ab873fdf70921ebd4a9cdae76739cbc722ef00c0b52db1d2364e298454f9a7e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.