Transformers
PyTorch
Czech
Czech
mbart
text2text-generation
abstractive summarization
mbart-cc25
Czech
Instructions to use krotima1/mbart-at2h-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krotima1/mbart-at2h-cs with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("krotima1/mbart-at2h-cs") model = AutoModelForSeq2SeqLM.from_pretrained("krotima1/mbart-at2h-cs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from krotima1/mbart-at2h-cs: direct link, hf CLI and curl.
- Browser
- Download file 2.44 GB
-
https://huggingface.co/krotima1/mbart-at2h-cs/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://krotima1/mbart-at2h-cs/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/krotima1/mbart-at2h-cs/resolve/main/pytorch_model.bin
2.44 GB
- Xet hash:
- 84205617313a9a3d76c706b0b7f595cf444eea3dee3655829e91d5baa39f0f10
- Size of remote file:
- 2.44 GB
- SHA256:
- 81379c8f6d463cb8128d8b7e189eb45e37dcaee697abfc8d8dbcfa75ec9bf0d1
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