Instructions to use natihash/vit_base_patch16_clip_224.text_lora16 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_lora16 with timm:
import timm model = timm.create_model("hf_hub:natihash/vit_base_patch16_clip_224.text_lora16", pretrained=True) - Transformers
How to use natihash/vit_base_patch16_clip_224.text_lora16 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_lora16") 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_lora16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01ee1344bf19faef2de8eaa3361f0d1b7fa87c4cd7ea625b4fa06094fd1bd797
- Size of remote file:
- 344 MB
- SHA256:
- c2be148a723a52da1c4ca4e5ae22e77a0351b29c912b52256c300c707e5fd95e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.