Instructions to use hf-tiny-model-private/tiny-random-PerceiverModel 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-PerceiverModel 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-PerceiverModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-PerceiverModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a652a35cd41f25f6bec2be3fe24960ec92413359bf6cd35a50c87c04a97e43d
- Size of remote file:
- 203 kB
- SHA256:
- 8c272b457e5609cdf37e60d81df32ec12853e32e0587539e94912d68ea72b982
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