Instructions to use natihash/vit_base_patch16_clip_224.text_lp 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_lp with timm:
import timm model = timm.create_model("hf_hub:natihash/vit_base_patch16_clip_224.text_lp", pretrained=True) - Transformers
How to use natihash/vit_base_patch16_clip_224.text_lp 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_lp") 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_lp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e2e66ceec37673111eac2b56c8d13bed26bda93f41442c2a4d714db49357715
- Size of remote file:
- 344 MB
- SHA256:
- 935d6a6ebbfe135ccc02aa121c3fb61405c878fae055b6ce865772a702964ecb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.