Instructions to use mbazaNLP/Quantized_Nllb_Finetuned_Edu_En_Kin_8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbazaNLP/Quantized_Nllb_Finetuned_Edu_En_Kin_8bit with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="mbazaNLP/Quantized_Nllb_Finetuned_Edu_En_Kin_8bit")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mbazaNLP/Quantized_Nllb_Finetuned_Edu_En_Kin_8bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f86bcb86fe58b6842df8f7f7223e230591309f0c224d74fbc73860868546b23f
- Size of remote file:
- 1.38 GB
- SHA256:
- c358da417be0d7cf4cd6b73fb5e0271747396e9155c7afa6aa41b4b86d3820c4
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