Instructions to use hf-tiny-model-private/tiny-random-FNetModel 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-FNetModel 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-FNetModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-FNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 134e5765d9557c375328352c41aeb82bba77492d379dbe16d7b99cdb91d51fff
- Size of remote file:
- 4.23 MB
- SHA256:
- 8692a8dc04aa4d07acc2e91f1c3d3e5f7a220e0297dbf2c19c43ba52d83bef7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.