Instructions to use masakhane/afri-mbart50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use masakhane/afri-mbart50 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("masakhane/afri-mbart50") model = AutoModelForSeq2SeqLM.from_pretrained("masakhane/afri-mbart50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from masakhane/afri-mbart50: direct link, hf CLI and curl.
- Browser
- Download file 2.44 GB
-
https://huggingface.co/masakhane/afri-mbart50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://masakhane/afri-mbart50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/masakhane/afri-mbart50/resolve/main/pytorch_model.bin
2.44 GB
- Xet hash:
- 1cfe5018a4cfba541f80b397e0c8e8d2f51d6de331fd9315b6e80e7f7f359550
- Size of remote file:
- 2.44 GB
- SHA256:
- 7c70ceafd8acb307dd0aa308ebcc7f9d668bbb9ff485454e83955685d282c0f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.