Instructions to use HuggingFaceH4/tiny-random-LlamaForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceH4/tiny-random-LlamaForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceH4/tiny-random-LlamaForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/tiny-random-LlamaForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceH4/tiny-random-LlamaForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6487ba1512ce9263fb3609afec9ef138a4c6c1a8ece67e2730938c7a66010bd9
- Size of remote file:
- 2.08 MB
- SHA256:
- 27df4e810c3f7338476d7ecc4ddb521f8dbdfe12ce4b2850a02b91c67f7a3a06
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