Instructions to use timm/vit_pe_lang_gigantic_patch14_448.fb 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 with timm:
import timm model = timm.create_model("hf_hub:timm/vit_pe_lang_gigantic_patch14_448.fb", pretrained=True) - Transformers
How to use timm/vit_pe_lang_gigantic_patch14_448.fb 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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_pe_lang_gigantic_patch14_448.fb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 73222e3838c04881499978dae8bcce2d1596911c1a9da8411daf6dc5766e4aa7
- Size of remote file:
- 6.96 GB
- SHA256:
- fb5587faa602ff632d5c51d3612c8f266a5996b4fd2edd387873054fc40cf4d6
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