Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen3_vl
image-text-to-text
multimodal embedding
qwen
embedding
Instructions to use tomaarsen/Qwen3-VL-Embedding-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/Qwen3-VL-Embedding-8B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/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 tomaarsen/Qwen3-VL-Embedding-8B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tomaarsen/Qwen3-VL-Embedding-8B") model = AutoModelForMultimodalLM.from_pretrained("tomaarsen/Qwen3-VL-Embedding-8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44a700ebb1b68f2392b0ef41f52fb98d59b8322f0c61768a076a4966fbc54c5e
- Size of remote file:
- 4.92 GB
- SHA256:
- a4da61f512e84fc0f0b80bcb7bcc5137eb3bf25b658a7d55f84f4056078545f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.