Instructions to use timm/vit_giantopt_patch16_siglip_gap_256.v2_webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_giantopt_patch16_siglip_gap_256.v2_webli with timm:
import timm model = timm.create_model("hf-hub:timm/vit_giantopt_patch16_siglip_gap_256.v2_webli", pretrained=True) - Transformers
How to use timm/vit_giantopt_patch16_siglip_gap_256.v2_webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_giantopt_patch16_siglip_gap_256.v2_webli")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_giantopt_patch16_siglip_gap_256.v2_webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_giantopt_patch16_siglip_gap_256.v2_webli: direct link, hf CLI and curl.
- Browser
- Download file 4.54 GB
-
https://huggingface.co/timm/vit_giantopt_patch16_siglip_gap_256.v2_webli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_giantopt_patch16_siglip_gap_256.v2_webli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_giantopt_patch16_siglip_gap_256.v2_webli/resolve/main/pytorch_model.bin
4.54 GB
- Xet hash:
- e3fb3ecf6bd99c90b9950a842a104c4b64f31650b97279c1e8bf8b2146d2504c
- Size of remote file:
- 4.54 GB
- SHA256:
- b441265ff2e1dff14a710d2abb98eb68c4d24797895ae70dfd519102fdc5956b
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