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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-Embedding-2B") model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3-VL-Embedding-2B", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Inconsitancies in model implementation
#24
by Samoed - opened
Hi, Qwen team!
It seems the implementation with SentenceTransformers is a bit different from what was reported. Previously in MTEB we had an implementation with your updates to the Transformers class, but later we decided to update it to use the SentenceTransformers version, but with it we got slightly different results than reported, while with the previous implementation we can reproduce them. You can see the full results comparison in this comment. This can be fixed by https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B/discussions/22