Instructions to use hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-GPTNeoXForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53e05abbd91eb93fc3016b15e743464e65da0ab85bb80ab835139db0b0b5b228
- Size of remote file:
- 1.6 MB
- SHA256:
- e7bc522b07a96d423ba50674d14b915b5cdffa1203516c1dc9e4c4b4c252088e
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