Instructions to use hf-tiny-model-private/tiny-random-EsmForMaskedLM 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-EsmForMaskedLM 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-EsmForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-EsmForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-EsmForMaskedLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88d0300806b3ab9a07f73d1298b67be66b7445d0961a73a61c438dc7a83339b5
- Size of remote file:
- 224 kB
- SHA256:
- 332bd18818efd18404bc59864ccfd439db24022d929bad287b0510eb5c14bad7
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