Instructions to use hf-tiny-model-private/tiny-random-PerceiverForMaskedLM 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-PerceiverForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-PerceiverForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d69642eef57b3b523b7dd1176af86130a515bd84dab7080eb30535e0d28c7868
- Size of remote file:
- 9.24 MB
- SHA256:
- 2bc8ba63c0b65801feb206576472d1d5d2757edd1a5448092a3cab878e84a508
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