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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce43a1c58a4e6966a2206fb14513a77e36598479d3d5cc1c1c747972ab83bf3e
- Size of remote file:
- 4.24 MB
- SHA256:
- fd24ee2fca32e167cf5db9664a488e838f4c2966b15dbce2f1fc28ec198b6e84
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