Instructions to use timm/vit_pe_lang_large_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_large_patch14_448.fb_tiling with timm:
import timm model = timm.create_model("hf_hub:timm/vit_pe_lang_large_patch14_448.fb_tiling", pretrained=True) - Transformers
How to use timm/vit_pe_lang_large_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_large_patch14_448.fb_tiling")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_pe_lang_large_patch14_448.fb_tiling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19426a3ab5edcd54d981cb45533a679b54b661f87a2b4fcaecc3b97194894b25
- Size of remote file:
- 1.17 GB
- SHA256:
- 0a24e3625d8c4aa1dd434b30d5248decdc77197155a3ac660aca078a989252bd
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