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:
- 664ccef7d93e057e9f3aeb0bb544e6a7f632f17b9e0b8540109295ad561a0410
- Size of remote file:
- 244 kB
- SHA256:
- dacc0446d47bf2ebc475d567a6bf2bd8fe4f1fedd35c481e833312a1675ab06a
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