Instructions to use hfl/rbtl3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/rbtl3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/rbtl3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/rbtl3") model = AutoModelForMaskedLM.from_pretrained("hfl/rbtl3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from hfl/rbtl3: direct link, hf CLI and curl.
- Browser
- Download file 244 MB
-
https://huggingface.co/hfl/rbtl3/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://hfl/rbtl3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/hfl/rbtl3/resolve/main/flax_model.msgpack
244 MB
- Xet hash:
- 0ec655aa3f432f1e1cca641fdc0f70c750b3cec61afe02c3488b666221984a63
- Size of remote file:
- 244 MB
- SHA256:
- d0bf87fb2d7b03b989d5baafbae6fb9ca3be2c9f083a41ee6d0fe3770646bc49
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