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
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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
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Update README.md (#1)
Verified signature- Update README.md (862af4a0170908be42f918378d6a9577df66f9e1) Co-authored-by: kiril <korallll@users.noreply.huggingface.co>71143ce
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