Instructions to use toolevalxm/MedAssistPro-TestRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use toolevalxm/MedAssistPro-TestRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="toolevalxm/MedAssistPro-TestRepo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("toolevalxm/MedAssistPro-TestRepo") model = AutoModelForSequenceClassification.from_pretrained("toolevalxm/MedAssistPro-TestRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from toolevalxm/MedAssistPro-TestRepo: direct link, hf CLI and curl.
- Browser
- Download file 10.2 kB
-
https://huggingface.co/toolevalxm/MedAssistPro-TestRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://toolevalxm/MedAssistPro-TestRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/toolevalxm/MedAssistPro-TestRepo/resolve/main/pytorch_model.bin
10.2 kB
- Xet hash:
- efa6a583deb8e9cb8b2515536ac4bde60b0d6ad1d463761ab2524c749f54c70a
- Size of remote file:
- 10.2 kB
- SHA256:
- f6387fc8e48aaebe79fde285dec6e1cc1fd23c7107fb36cf05b987479b17827e
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