Instructions to use toolevalxm/MedVision-X-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-X-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-X-TestRepo") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("toolevalxm/MedVision-X-TestRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-X-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from toolevalxm/MedVision-X-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 36 Bytes
-
https://huggingface.co/toolevalxm/MedVision-X-TestRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://toolevalxm/MedVision-X-TestRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/toolevalxm/MedVision-X-TestRepo/resolve/main/pytorch_model.bin
36 Bytes
- Xet hash:
- 7df3bf334a360642770a2eac2da9bb0ddeefea62fadfebb86a17fd6e7b3d92d3
- Size of remote file:
- 36 Bytes
- SHA256:
- 2ea13bbbc6c1a98ba0c206243e7543f5070ba161e99be86f9fd4def40e1015ac
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