Instructions to use toolevalxm/MedVision-DiagNet-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-DiagNet-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-DiagNet-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-DiagNet-TestRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-DiagNet-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from toolevalxm/MedVision-DiagNet-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 23 Bytes
-
https://huggingface.co/toolevalxm/MedVision-DiagNet-TestRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://toolevalxm/MedVision-DiagNet-TestRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/toolevalxm/MedVision-DiagNet-TestRepo/resolve/main/pytorch_model.bin
23 Bytes
- Xet hash:
- 3a02de3fa74fbcde2bafa3c0b1b5c8ab68d0187626f28328c15bb697e63116e2
- Size of remote file:
- 23 Bytes
- SHA256:
- f0aec1ff9fe2c88553e6bc4e167aaeaed5e8ed3821a3caf5862e9cb38c8277bc
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