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
支持多图吗
#12
by chaxjli - opened
您好,请问这个模型支持多张图片嘛, 我如果修改这个代码的话Qwen3-VL-Embedding-2B/scripts/qwen3_vl_embedding.py,模型有这个能力吗
您好,请问这个模型支持多张图片嘛, 我如果修改这个代码的话Qwen3-VL-Embedding-2B/scripts/qwen3_vl_embedding.py,模型有这个能力吗
I guess not, but you can generate from two images two embeddings. Then aggregate them together to one. Maybe that can solve your problem.