Instructions to use natihash/vit_base_patch16_clip_224.text_fft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use natihash/vit_base_patch16_clip_224.text_fft with timm:
import timm model = timm.create_model("hf_hub:natihash/vit_base_patch16_clip_224.text_fft", pretrained=True) - Transformers
How to use natihash/vit_base_patch16_clip_224.text_fft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="natihash/vit_base_patch16_clip_224.text_fft") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("natihash/vit_base_patch16_clip_224.text_fft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e60b6b475b1bdafc61c6d373d337eaf29b96b0aade59d5f7868b3e7a4a844ca
- Size of remote file:
- 344 MB
- SHA256:
- a4d46f42a1c3ce7b9bde68e34acfa51cd0e95066c026bcef7fd9c61f3eb46503
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