Instructions to use donal/Pro_Berta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use donal/Pro_Berta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="donal/Pro_Berta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("donal/Pro_Berta") model = AutoModelForMaskedLM.from_pretrained("donal/Pro_Berta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from donal/Pro_Berta: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/donal/Pro_Berta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://donal/Pro_Berta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/donal/Pro_Berta/resolve/main/flax_model.msgpack
334 MB
- Xet hash:
- 3b83763adc89801dc289da7be10fd38820c9084f2092bfd483e17e7fa2b40e01
- Size of remote file:
- 334 MB
- SHA256:
- 8fb19a370b542b707ac8de296a7a052af6229721f00068537e29aedb916f8db8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.