Instructions to use hf-tiny-model-private/tiny-random-XmodForMaskedLM 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-XmodForMaskedLM 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-XmodForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XmodForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-XmodForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb242b84f954e0c28ab59df88803bbe3bae2672435ac1c50c3cfcd4913f8ff0b
- Size of remote file:
- 33.3 MB
- SHA256:
- 442eb564a399392d2b0008c00c9ccc4c8380ad287d1752f0620860f5dd33d238
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