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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- faa99900d7da2f406a65db7b811c7b91be363f57fa46ec07fd264e2f17dd9083
- Size of remote file:
- 3.28 MB
- SHA256:
- 1966e7727508d6f4d35e19178140a499bde92491cb711a4daffa0f7fc3349d7d
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