Instructions to use sgugger/debug-example2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/debug-example2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/debug-example2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/debug-example2") model = AutoModelForSequenceClassification.from_pretrained("sgugger/debug-example2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sgugger/debug-example2: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/sgugger/debug-example2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sgugger/debug-example2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sgugger/debug-example2/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- c57c8c71e77d62e85eaf20195412d3e8ac57c429e76b9b15090c7017a47440c6
- Size of remote file:
- 268 MB
- SHA256:
- d7537f85bb36e0377023770525fee450ac0b34656eae79446e9a9235cffa2ebd
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