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:
- adb5401aaad4d42e1ec1ad4a543cdf7c80e6dc86af9588a1bafb4532bfa113a5
- Size of remote file:
- 156 MB
- SHA256:
- 3e04f7477f55dffce2a2fbc4d0ba35068415162a9e92e3d5cc74a49781ba4eb0
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