Instructions to use Python/ACROSS-m2o-eng-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Python/ACROSS-m2o-eng-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Python/ACROSS-m2o-eng-small") model = AutoModelForSeq2SeqLM.from_pretrained("Python/ACROSS-m2o-eng-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Python/ACROSS-m2o-eng-small: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/Python/ACROSS-m2o-eng-small/resolve/bbb3240fc5fc2a9146fbb77c6bd764bb60ae03cb/pytorch_model.bin
- Command line
-
hf download hf://Python/ACROSS-m2o-eng-small@bbb3240fc5fc2a9146fbb77c6bd764bb60ae03cb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Python/ACROSS-m2o-eng-small/resolve/bbb3240fc5fc2a9146fbb77c6bd764bb60ae03cb/pytorch_model.bin
1.2 GB
- Xet hash:
- 39a11bf16df2eadb8b5752b8ea125e1ad152ffcde412e2427572c9bcc4c29f29
- Size of remote file:
- 1.2 GB
- SHA256:
- 072be6ebd5cbd42c3bd7a1a6ed1ea9f54c8af2a3fd8d9e109d6816fc882d7903
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