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:
- 481ceb25756149ad8bc50e4d7ea9cd2933a9d7972821852e8608418780a569f1
- Size of remote file:
- 342 kB
- SHA256:
- 4aab5adb4d59d80544fe87c5492cbdbd1369002346d9e17c069cc88bdbc4da9f
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