Feature Extraction
Transformers
Safetensors
avito_gated_fusion
siglip
siglip2
vision
text
clip
multimodal
image-text-embeddings
pet-recognition
custom_code
Instructions to use AvitoTech/SigLIP2-giant-e5small-v2-gating with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AvitoTech/SigLIP2-giant-e5small-v2-gating with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AvitoTech/SigLIP2-giant-e5small-v2-gating", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AvitoTech/SigLIP2-giant-e5small-v2-gating", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Fix AutoModel loading, add image processor and Apache-2.0 license (#2)
- Fix AutoModel loading, add image processor and Apache-2.0 license (c98d3b86d283e12867297ae34e1a76ec5612dcb7) Co-authored-by: Vasiliy kudryavtsev <Slenser0@users.noreply.huggingface.co>b6a47c3