Instructions to use RDCPP/bart_en2de_training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RDCPP/bart_en2de_training with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RDCPP/bart_en2de_training") model = AutoModelForSeq2SeqLM.from_pretrained("RDCPP/bart_en2de_training", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RDCPP/bart_en2de_training: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/RDCPP/bart_en2de_training/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RDCPP/bart_en2de_training/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RDCPP/bart_en2de_training/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- cc019e6a8221149caf8c3fca287b4cefd0c949fe66b595c493e1f5939b84280b
- Size of remote file:
- 1.63 GB
- SHA256:
- 530d6c0d9b418b025d0a2bfa56c5e5649a606c589b940d879e0ea60436b42502
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