Instructions to use hfl/rbt4-h312 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/rbt4-h312 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/rbt4-h312")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbt4-h312") model = AutoModelForMaskedLM.from_pretrained("hfl/rbt4-h312", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81a1975a73994a219e6666c97a0f461086f4d631d1d321c3ff9651a501f89dd5
- Size of remote file:
- 45.8 MB
- SHA256:
- 02bf4175948cd28a4de865e0400c0e221baa8bfd28ca105fac84161e42f75f51
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