Instructions to use KMayanja/nllb-600M-medical-luganda-bidirectional with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use KMayanja/nllb-600M-medical-luganda-bidirectional with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M") model = PeftModel.from_pretrained(base_model, "KMayanja/nllb-600M-medical-luganda-bidirectional") - Transformers
How to use KMayanja/nllb-600M-medical-luganda-bidirectional with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KMayanja/nllb-600M-medical-luganda-bidirectional", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c602c5909813b1addafd514faa5045db8d0a0ea9bae7bef419c8848022ea13d0
- Size of remote file:
- 28.3 MB
- SHA256:
- ce5f1d3ad7c700a177d0bee2a48f90b0fda0a96728d564bf2dbc8d8810905968
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.