Instructions to use olm/olm-roberta-base-latest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use olm/olm-roberta-base-latest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="olm/olm-roberta-base-latest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("olm/olm-roberta-base-latest") model = AutoModelForMaskedLM.from_pretrained("olm/olm-roberta-base-latest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from olm/olm-roberta-base-latest: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/olm/olm-roberta-base-latest/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://olm/olm-roberta-base-latest/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/olm/olm-roberta-base-latest/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 8e142a2c6ff30e9a34cceb649ccc70c807494a725a531aabeb2fbb06cdd7494d
- Size of remote file:
- 499 MB
- SHA256:
- 6d4a6603680a1cbfc2d21ff0e20cb2932be649e5db2cddf8172c9b777a5f247e
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