Instructions to use hfl/rbt4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/rbt4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/rbt4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbt4") model = AutoModelForMaskedLM.from_pretrained("hfl/rbt4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b12dd94e9bd7e9e2d66786ff0c33ebc98e631b0d281da913b1dcf5618bd9878
- Size of remote file:
- 185 MB
- SHA256:
- 4df7442a7e19328c43c38db7a6d57bc2c0113eda659d8369b4e35e8af757c3f1
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