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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f3e0929021165a558859714c0a25a87c34706b41aaf1db828205bd1a9d43422
- Size of remote file:
- 223 kB
- SHA256:
- 52d630fb1984ee71bc470f49f7d98aaf0e10707d27537a48decea49113b10a7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.