Instructions to use toolevalxm/MedVision-DiagnosticsAI-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedVision-DiagnosticsAI-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="toolevalxm/MedVision-DiagnosticsAI-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-DiagnosticsAI-TestRepo") model = AutoModelForImageClassification.from_pretrained("toolevalxm/MedVision-DiagnosticsAI-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from toolevalxm/MedVision-DiagnosticsAI-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 2.57 kB
-
https://huggingface.co/toolevalxm/MedVision-DiagnosticsAI-TestRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://toolevalxm/MedVision-DiagnosticsAI-TestRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/toolevalxm/MedVision-DiagnosticsAI-TestRepo/resolve/main/pytorch_model.bin
2.57 kB
- Xet hash:
- 8dc7e2698f8fec54acaf180a9614bcb8c20d247c78d09b17491da90980a90a3a
- Size of remote file:
- 2.57 kB
- SHA256:
- 1c0427ba2750594566e572217c24249d3d7cdba2506df27b01d0969d4ac705c8
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