Instructions to use melvin127/nllb-200-en-runyoro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use melvin127/nllb-200-en-runyoro with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("melvin127/nllb-200-en-runyoro") model = AutoModelForSeq2SeqLM.from_pretrained("melvin127/nllb-200-en-runyoro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from melvin127/nllb-200-en-runyoro: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/melvin127/nllb-200-en-runyoro/resolve/main/tokenizer.json
- Command line
-
hf download hf://melvin127/nllb-200-en-runyoro/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/melvin127/nllb-200-en-runyoro/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- aba09a6be85d8b10f496f3ee8714a44a86190bf438ca94cef4bbbf921535840d
- Size of remote file:
- 32.2 MB
- SHA256:
- 570293b084645660e4881a8acf17c2c28dac07a64b17cc0dca332ef61fb1b0b9
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