Instructions to use timm/vit_pe_lang_gigantic_patch14_448.fb_tiling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_pe_lang_gigantic_patch14_448.fb_tiling with timm:
import timm model = timm.create_model("hf-hub:timm/vit_pe_lang_gigantic_patch14_448.fb_tiling", pretrained=True) - Transformers
How to use timm/vit_pe_lang_gigantic_patch14_448.fb_tiling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_pe_lang_gigantic_patch14_448.fb_tiling")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_pe_lang_gigantic_patch14_448.fb_tiling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/vit_pe_lang_gigantic_patch14_448.fb_tiling: direct link, hf CLI and curl.
- Browser
- Download file 6.96 GB
-
https://huggingface.co/timm/vit_pe_lang_gigantic_patch14_448.fb_tiling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/vit_pe_lang_gigantic_patch14_448.fb_tiling/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/vit_pe_lang_gigantic_patch14_448.fb_tiling/resolve/main/pytorch_model.bin
6.96 GB
- Xet hash:
- f8500469f1de7c0b5be47a6e522aaeab6f4efa9af3b34a1c501ee6318790bbd1
- Size of remote file:
- 6.96 GB
- SHA256:
- 28ac210445d5befbe49fe5dadb83ba8c2e33b91e383bed9bc400dda5cc8cf2a1
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