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:
- 2350202647ec7a68d50f70abb20611f0ce5d97863bd2abec952b6cdfc658ac58
- Size of remote file:
- 124 kB
- SHA256:
- 600e3341cb3a83e3859b01d2936e8897a2976606002399cb9191906f7a624083
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