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:
- 9d12648980ccdd100cc851f293a4c01f7c4a2ee1f68dd11a7511f1a12a07920b
- Size of remote file:
- 242 kB
- SHA256:
- 1553c6f849d4e9710a43bd9d3d6404f3bd8b568972e17cc9c0b50d0fb63ce961
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