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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b5bb95bd189c4a5820e0912106a462e3f93a7c73f810749e843eb08763a8641
- Size of remote file:
- 154 MB
- SHA256:
- 3c6a0ead23bfb0b274621dc9356a918418be138aeeca32f448bc4511f657879a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.