Instructions to use trl-internal-testing/tiny-LlamaForSequenceClassification-3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-LlamaForSequenceClassification-3.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="trl-internal-testing/tiny-LlamaForSequenceClassification-3.2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-LlamaForSequenceClassification-3.2") model = AutoModelForSequenceClassification.from_pretrained("trl-internal-testing/tiny-LlamaForSequenceClassification-3.2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f2d9b8be59de9596cb8b057ac343c63a223c9393e9c2662d174a36ab14387dd
- Size of remote file:
- 4.16 MB
- SHA256:
- 30edd29f6a9bab80d293849f4dbf37b9b9826362953e177f4a29964a1cb839a2
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