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This model is a 14.75% smaller version of google/siglip2-giant-opt-patch16-384 optimized for Japanese language via vocabulary size reduction using the trimming method. This trimmed model should perform similarly to...
Fonte do modelo
Descrição da fonte
This model is a 14.75% smaller version of google/siglip2-giant-opt-patch16-384 optimized for Japanese language via vocabulary size reduction using the method. This trimmed model should perform similarly to the original model with only 16,384 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.
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1 fonteVerificado 10 de set.
Artefatos de modelo
2 artefatosTrechos de fonte
2 trechos| Metric | Original | Trimmed | Reduction |
|---|---|---|---|
| Vocabulary size | 256,000 tokens | 16,384 tokens | 93.60% |
| Model size | 1,871,885,426 params | 1,595,847,794 params | 14.75% |
image
from transformers import pipeline
# load pipeline
image_classifier = pipeline(model="alphaedge-ai/siglip2-giant-opt-patch16-384-jpn-16384", task="zero-shot-image-classification")
# load image and candidate labels
image = "http://images.cocodataset.org/val2017/000000039769.jpg"
candidate_labels = ["Potential label 1 in Japanese", "Potential label 2 in Japanese", "Potential label 3 in Japanese", "Potential label 4 in Japanese"]
# run inference
outputs = image_classifier(image, candidate_labels)
print(outputs)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("alphaedge-ai/siglip2-giant-opt-patch16-384-jpn-16384")
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 = ["Text 1 in Japanese", "Text 2 in Japanese", "Text 3 in Japanese", "Text 4 in Japanese"]
image_embeddings = model.encode(images)
text_embeddings = model.encode(texts)
print(image_embeddings.shape, text_embeddings.shape)
similarities = model.similarity(image_embeddings, text_embeddings)
print(similarities)
@misc{tschannen2025siglip2multilingualvisionlanguage,
title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features},
author={Michael Tschannen and Alexey Gritsenko and Xiao Wang and Muhammad Ferjad Naeem and Ibrahim Alabdulmohsin and Nikhil Parthasarathy and Talfan Evans and Lucas Beyer and Ye Xia and Basil Mustafa and Olivier Hénaff and Jeremiah Harmsen and Andreas Steiner and Xiaohua Zhai},
year={2025},
eprint={2502.14786},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.14786},
}
@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},
}
--- pipeline_tag: zero-shot-image-classification language: jpn license: apache-2.0 tags: - trimmed library_name: sentence-transformers base_model: google/siglip2-giant-opt-patch16-384 base_model_relation: quantized datasets: - lbourdois/fineweb-2-trimming --- # siglip2-giant-opt-patch16-384-jpn-16384 This model is a **14.75%** smaller version of [google/siglip2-giant-opt-patch16-384](https://huggingface.co/google/siglip2-giant-opt-patch16-384) optimized for **Japanese** language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/lbourdois/introduction-to-trimming) method. This trimmed model should perform similarly to the original model with only 16,384 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 | Metric | Original | Trimmed | Reduction | |--------|----------|---------|-----------| | **Vocabulary size** | 256,000 tokens | 16,384 tokens | **93.60%** | | **Model size** | 1,871,885,426 params | 1,595,847,794 params | **14.75%** |  ## Mining Dataset Statistics - **Number of texts used for mining**: 200,000 texts - **Dataset**: [lbourdois/fineweb-2-trimming](https://huggingface.co/datasets/lbourdois/fineweb-2-trimming) ## Usage #### Transformers (zero-shot image classification) ```python from transformers import pipeline # load pipeline image_classifier = pipeline(model="alphaedge-ai/siglip2-giant-opt-patch16-384-jpn-16384", task="zero-shot-image-classification") # load image and candidate labels image = "http://images.cocodataset.org/val2017/000000039769.jpg" candidate_labels = ["Potential label 1 in Japanese", "Potential label 2 in Japanese", "Potential label 3 in Japanese", "Potential label 4 in Japanese"] # run inference outputs = image_classifier(image, candidate_labels) print(outputs) ``` #### Sentence-transformers (texts-images similarity) ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("alphaedge-ai/siglip2-giant-opt-patch16-384-jpn-16384") images = [ "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg", "https://hugg...
Source context: 4 downloads · 0 likes · Pipeline zero-shot-image-classification · Library sentence-transformers · Repo alphaedge-ai/siglip2-giant-opt-patch16-384-jpn-16384