Instructions to use nllg/bygpt5-small-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nllg/bygpt5-small-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nllg/bygpt5-small-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("nllg/bygpt5-small-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nllg/bygpt5-small-en: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/nllg/bygpt5-small-en/resolve/main/training_args.bin
- Command line
-
hf download hf://nllg/bygpt5-small-en/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nllg/bygpt5-small-en/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 98a3eeddf1f70f4c771f022c56369abc79c3c28b00e3c083805c94861821264c
- Size of remote file:
- 3.44 kB
- SHA256:
- fd7d487c64bf39caf20454233b3d9e453d337ccf878aef265c3d01252df2b3c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.