Instructions to use hfl/rbt6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/rbt6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/rbt6")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbt6") model = AutoModelForMaskedLM.from_pretrained("hfl/rbt6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac92c3098e0588cc51b33f6df54fc9ecfd46000ff8f8a300f15bbcf412ab55d0
- Size of remote file:
- 241 MB
- SHA256:
- b5341c17ef5f3b028d0b2aa74d13ea5851ef17e456864ddffae3e84e5abd4109
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