Instructions to use Python/ACROSS-m2o-eng-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Python/ACROSS-m2o-eng-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Python/ACROSS-m2o-eng-base") model = AutoModelForSeq2SeqLM.from_pretrained("Python/ACROSS-m2o-eng-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from Python/ACROSS-m2o-eng-base: direct link, hf CLI and curl.
- Browser
- Download file 309 Bytes
-
https://huggingface.co/Python/ACROSS-m2o-eng-base/resolve/refs%2Fpr%2F1/.gitattributes
- Command line
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hf download hf://Python/ACROSS-m2o-eng-base@refs/pr/1/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/Python/ACROSS-m2o-eng-base/resolve/refs%2Fpr%2F1/.gitattributes
309 Bytes
| pytorch_model.bin filter=lfs diff=lfs merge=lfs -text | |
| optimizer.pt filter=lfs diff=lfs merge=lfs -text | |
| scheduler.pt filter=lfs diff=lfs merge=lfs -text | |
| spiece.model filter=lfs diff=lfs merge=lfs -text | |
| training_args.bin filter=lfs diff=lfs merge=lfs -text | |
| model.safetensors filter=lfs diff=lfs merge=lfs -text | |