Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen3_vl
image-text-to-text
multimodal embedding
qwen
embedding
Instructions to use Qwen/Qwen3-VL-Embedding-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qwen/Qwen3-VL-Embedding-2B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Qwen/Qwen3-VL-Embedding-2B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-Embedding-2B") model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3-VL-Embedding-2B") - Notebooks
- Google Colab
- Kaggle
Add trust_remote_code support via AutoModel integration
#22
by whybe-choi - opened
Hi Qwen team, thank you for releasing this great embedding model!
I'd like to propose adding trust_remote_code support so that the community can load the model more conveniently.
The current repository provides scripts/qwen3_vl_embedding.py as a helper wrapper,
but this requires users to manually clone the repo and import from the script directly β
which adds friction compared to the standard HuggingFace workflow.
By adding trust_remote_code support with proper auto_map configuration, users in the
community can now load the model with just:
from transformers import AutoModel, AutoProcessor
model = AutoModel.from_pretrained("Qwen/Qwen3-VL-Embedding-2B", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-Embedding-2B", trust_remote_code=True)
embeddings = model.encode(inputs)