Instructions to use hf-tiny-model-private/tiny-random-EsmModel 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-EsmModel 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-EsmModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-EsmModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-EsmModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 140d70951abc685f78060b3295bb6f6976e6ff36dab1ad8a43af0b41d1dd3d69
- Size of remote file:
- 332 kB
- SHA256:
- 465233d4fa86801902b7ce89ad88bd5678dbd2c26cdb40ed9c6088795e447210
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