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:
- 1f045bdbb4c8e57f2fcd16fc80a4717af01b52dfd23bd38b8e349cd378d6b2bc
- Size of remote file:
- 239 MB
- SHA256:
- a746b6e105f92bf1d3f6444712635288270fed570cae363ad73c29f92836f327
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