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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbt4") model = AutoModelForMaskedLM.from_pretrained("hfl/rbt4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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upload flax model
a62c22d -
allow flax
0d7172c
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add fast tokenizer config
hfl-rccde4e10
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Update config.json
69663d0
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update info
ymcui639a3e7 -
First version of the rbt4 model and tokenizer.
ymcuide0b1a2 -
initial commit
a730cfb