Sentence Similarity
Transformers
Safetensors
sentence-transformers
English
multilingual
qwen3_vl
image-text-to-text
multimodal
embeddings
vision-language
awq
int4
w4a16
compressed-tensors
vllm
Instructions to use gonuit/Qwen3-VL-Embedding-8B-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gonuit/Qwen3-VL-Embedding-8B-AWQ-4bit with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("gonuit/Qwen3-VL-Embedding-8B-AWQ-4bit") model = AutoModelForMultimodalLM.from_pretrained("gonuit/Qwen3-VL-Embedding-8B-AWQ-4bit") - sentence-transformers
How to use gonuit/Qwen3-VL-Embedding-8B-AWQ-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gonuit/Qwen3-VL-Embedding-8B-AWQ-4bit") 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] - Notebooks
- Google Colab
- Kaggle
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