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:
- cb803c9950eba5aba0e1ecb57e8b8e400fdad62168310bd973224fdfd2da7494
- Size of remote file:
- 3.34 MB
- SHA256:
- 5584b4927b3a0580c41513bc63abb95cd972852ea9327c81fb9a9484cfe95758
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