Instructions to use joelb/custom-handler-tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joelb/custom-handler-tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joelb/custom-handler-tutorial")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joelb/custom-handler-tutorial") model = AutoModelForSequenceClassification.from_pretrained("joelb/custom-handler-tutorial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from joelb/custom-handler-tutorial: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/joelb/custom-handler-tutorial/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://joelb/custom-handler-tutorial@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/joelb/custom-handler-tutorial/resolve/refs%2Fpr%2F1/model.safetensors
268 MB
- Xet hash:
- b231d09fdc6b334ccbfb9da1044f91e6aac1839791ccbded3a6e4d99de807ca7
- Size of remote file:
- 268 MB
- SHA256:
- a2e46512c3fe95064cc92cd00d7c52a96be25ac6d753ee736e93438f8aa6a09e
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