clip-ViT-B-32-multilingual-v1-sr-32768

clip-ViT-B-32-multilingual-v1-sr-32768

This model is a smaller version of sentence-transformers/clip-ViT-B-32-multilingual-v1 optimized for Serbian language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.

Model Statistics

Model size (with visual encoder)

Model size (without visual encoder)

Mining Dataset Statistics

• Number of texts used for mining: 200,000 texts
• Dataset: lbourdois/fineweb-2-trimming

Usage

Sentence-transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("aphaedge-ai/clip-ViT-B-32-multilingual-v1-sr-32768")
clip = SentenceTransformer("sentence-transformers/clip-ViT-B-32")

images = [
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
    "https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg"
]
texts = ["Potential label 1 in Serbian", "Potential label 2 in Serbian", "Potential label 3 in Serbian", "Potential label 4 in Serbian"]

image_embeddings = clip.encode(images)
text_embeddings = model.encode(texts)
print(image_embeddings.shape, text_embeddings.shape)

similarities = model.similarity(image_embeddings, text_embeddings)
print(similarities)

Citations

clip-vit-b-32-multilingual-v1

Hugging Face repo: https://huggingface.co/sentence-transformers/clip-ViT-B-32-multilingual-v1

Trimming blog post

@misc{hf_blogpost_trimming,
      title={Introduction to Trimming}, 
      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
      year={2026},
      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming}, 
}