Instructions to use hf-internal-testing/tiny-random-PerceiverForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PerceiverForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-PerceiverForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-PerceiverForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-PerceiverForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f27d7480378898ba14c8221333b78cf2c47ddfecef59db10e0ad9ee40e31514
- Size of remote file:
- 9.24 MB
- SHA256:
- 2eb34d8417d8cc0b215132931b9f27a6e0401c4e452de21ca729ddad7f005492
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