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:
- 74562a38e7dd2fb41fb3a487af17f0c3383f4f5d5b88d55421efdff405ee3c6b
- Size of remote file:
- 182 MB
- SHA256:
- c6c65653ad09fa8fbc9411225566b255394c1ea3de4d3c3f060c4d23b49299a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.