Instructions to use hfl/rbt3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/rbt3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/rbt3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbt3") model = AutoModelForMaskedLM.from_pretrained("hfl/rbt3") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e10a40f6ca66b54a73cb0b940d5033191278cc161909d4039995a47a80f48201
- Size of remote file:
- 154 MB
- SHA256:
- 61171bca62daefbca3949cb811e5534b243f254d3dff76e0a7ed6ce1311868c7
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