Instructions to use chanyaphas/injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chanyaphas/injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chanyaphas/injection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chanyaphas/injection") model = AutoModelForSequenceClassification.from_pretrained("chanyaphas/injection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from chanyaphas/injection: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/chanyaphas/injection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://chanyaphas/injection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/chanyaphas/injection/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- aab4463373fb41e32a35a814c5b4188c228ea63baae3b2696c393422ae3385ed
- Size of remote file:
- 738 MB
- SHA256:
- 3bbbcafb1687de145be4e1e9d6a020d6a882e9829e1811f5c3c0bcc9e940e445
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