Instructions to use hf-tiny-model-private/tiny-random-OPTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-OPTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-OPTModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-OPTModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-OPTModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 313d84a033bf609b3ce7ca0498065488d29dca233d24eb1a795e43a64fd1d545
- Size of remote file:
- 192 kB
- SHA256:
- 348e239d5fb1b85a63fbe2da259f8955779dc6d02e749bfbc5987098b47f1164
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