Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen3_vl
image-text-to-text
multimodal embedding
qwen
embedding
Instructions to use abdebug2003/qwen3-vl-embedding-endpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use abdebug2003/qwen3-vl-embedding-endpoint with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("abdebug2003/qwen3-vl-embedding-endpoint") 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 abdebug2003/qwen3-vl-embedding-endpoint with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("abdebug2003/qwen3-vl-embedding-endpoint") model = AutoModelForMultimodalLM.from_pretrained("abdebug2003/qwen3-vl-embedding-endpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 771 Bytes
ea57de7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"transformer_task": "feature-extraction",
"modality_config": {
"text": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"image": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"video": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"message": {
"method": "forward",
"method_output_name": "last_hidden_state",
"format": "structured"
}
},
"module_output_name": "token_embeddings",
"processing_kwargs": {
"chat_template": {
"add_generation_prompt": true
}
},
"unpad_inputs": false
}
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