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
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Download README.md from timm/vit_pe_lang_gigantic_patch14_448.fb_tiling: direct link, hf CLI and curl.
- Browser
- Download file 162 Bytes
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https://huggingface.co/timm/vit_pe_lang_gigantic_patch14_448.fb_tiling/resolve/main/README.md
- Command line
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hf download hf://timm/vit_pe_lang_gigantic_patch14_448.fb_tiling/README.md
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curl -L -o README.md https://huggingface.co/timm/vit_pe_lang_gigantic_patch14_448.fb_tiling/resolve/main/README.md
162 Bytes
| tags: | |
| - image-feature-extraction | |
| - timm | |
| - transformers | |
| library_name: timm | |
| license: apache-2.0 | |
| # Model card for vit_pe_lang_gigantic_patch14_448.fb_tiling | |