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")# Load model directly from transformers import AutoModelWithLMHead model = AutoModelWithLMHead.from_pretrained("nllg/bygpt5-small-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nllg/bygpt5-small-en: direct link, hf CLI and curl.
- Browser
- Download file 294 MB
-
https://huggingface.co/nllg/bygpt5-small-en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nllg/bygpt5-small-en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nllg/bygpt5-small-en/resolve/main/pytorch_model.bin
294 MB
- Xet hash:
- 7748027dff07fbc10684226d292141cecc736a069e99c948bedb0135dabe0d69
- Size of remote file:
- 294 MB
- SHA256:
- c34bb4aa71ee1759dea189b24208e725b230520d0d73a2337180c873e2ec5751
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.