Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen3_vl
image-text-to-text
multimodal embedding
qwen
embedding
Instructions to use Qwen/Qwen3-VL-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Qwen/Qwen3-VL-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-8B") 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-8B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-Embedding-8B") model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen3-VL-Embedding-8B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2b9da31984e15700c1b093393f90b8999d21c70f8b68b50681150b9d31f2d56
- Size of remote file:
- 1.46 GB
- SHA256:
- 000213b6d1d03ed9023fac23716da51ec4c5be221a04526c2f732d31d8fed1f5
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