Instructions to use hf-internal-testing/tiny-random-PerceiverModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PerceiverModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-PerceiverModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PerceiverModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-PerceiverModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52ef5d78b1c13ea138e4a1966ec707cb88a60ef32b9aa2dcebbd7195631d9619
- Size of remote file:
- 193 kB
- SHA256:
- 9a36c57440da654f704681fb20fdbe7dfb7570ac9bc4a0c9bd9027e1091aab62
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