Instructions to use hf-internal-testing/tiny-random-OPTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-OPTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-OPTModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-OPTModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-OPTModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23fac38859e572f4b2ae926fdec52e651989b9232914e2d8e34aa3aaa405709e
- Size of remote file:
- 3.26 MB
- SHA256:
- 0a589403c79b3cc4d9ed1d99194446964a3b45b3b6746785d18b6cce87413e28
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