Instructions to use timm/vit_pe_lang_large_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_large_patch14_448.fb with timm:
import timm model = timm.create_model("hf_hub:timm/vit_pe_lang_large_patch14_448.fb", pretrained=True) - Transformers
How to use timm/vit_pe_lang_large_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_large_patch14_448.fb")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_pe_lang_large_patch14_448.fb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and Github link to metadata
#1
by nielsr HF Staff - opened
This PR adds the image-feature-extraction pipeline tag to the model card, ensuring people can find your model at https://huggingface.co/models?pipeline_tag=image-feature-extraction&sort=trending. It also adds the Github link to the metadata.