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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from hfl/rbt4: direct link, hf CLI and curl.
- Browser
- Download file 182 MB
-
https://huggingface.co/hfl/rbt4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://hfl/rbt4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/hfl/rbt4/resolve/main/flax_model.msgpack
182 MB
- Xet hash:
- 74562a38e7dd2fb41fb3a487af17f0c3383f4f5d5b88d55421efdff405ee3c6b
- Size of remote file:
- 182 MB
- SHA256:
- c6c65653ad09fa8fbc9411225566b255394c1ea3de4d3c3f060c4d23b49299a1
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