Instructions to use TSjB/NLLB-201-600M-QM-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TSjB/NLLB-201-600M-QM-V1 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="TSjB/NLLB-201-600M-QM-V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("TSjB/NLLB-201-600M-QM-V1") model = AutoModelForSeq2SeqLM.from_pretrained("TSjB/NLLB-201-600M-QM-V1") - Notebooks
- Google Colab
- Kaggle
Authors: Bogdan Tewunalany, Ali Berberov
Github
As a base we took NLLB-200-600M model and trained it on 265718 parallel sentences from Qarachay-Malqar to russian language.
Where to use:
HF
Site
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