Instructions to use timm/repvit_m2.dist_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/repvit_m2.dist_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/repvit_m2.dist_in1k", pretrained=True) - Transformers
How to use timm/repvit_m2.dist_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/repvit_m2.dist_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/repvit_m2.dist_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/repvit_m2.dist_in1k: direct link, hf CLI and curl.
- Browser
- Download file 35.7 MB
-
https://huggingface.co/timm/repvit_m2.dist_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/repvit_m2.dist_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/repvit_m2.dist_in1k/resolve/main/pytorch_model.bin
35.7 MB
- Xet hash:
- ce4dd8dbb6ba727ee987921e9b09e58878d9c16f79ac7c256c18741b540f2a49
- Size of remote file:
- 35.7 MB
- SHA256:
- e5610392bccb287f8506a9c2ba8d4ed3e5c3be9c146f67174f79dcc4682abe93
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