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
Verified signatureHi, and thanks for releasing these models. This follows up on the [report on the SigLIP2-Base repo](https://huggingface.co/AvitoTech/SigLIP2-Base-for-animal-identification/discussions). - **The architecture was not loadable.** `config.json` advertised this composite checkpoint as a plain `SiglipModel`, so `AutoModel` built a differently shaped model and initialised it randomly. The repo now ships `configuration_avito_gated.py` and `modeling_avito_gated.py` -- a faithful port of the `FaceRecognizer` class from the model card -- plus a `config.json` that describes both towers. **Tensor names are unchanged**, so existing `load_state_dict` code keeps working. Also: the download snippet in the card points at `AvitoTech/SigLIP2-giant-e5small-v2-gating-for-animal-identification`, which 404s. It now points here. - **Image processor.** Added `preprocessor_config.json`, copied unchanged from `google/siglip2-giant-opt-patch16-384`, so the repo is self-contained and the card no longer has to s…c98d3b8