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 pytorch_model.bin from joelb/custom-handler-tutorial: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/joelb/custom-handler-tutorial/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://joelb/custom-handler-tutorial/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/joelb/custom-handler-tutorial/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 80e87e908d165644f56d0cf8e0a9b91f73e50483f277b45ec9abfd4fca63b1f1
- Size of remote file:
- 268 MB
- SHA256:
- 5aa7398d830fcc94f95af88d7cc3013813668cfc58a07d75a8116cfd8af75c4d
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