Instructions to use eshwarZugz/bart_large-paranmt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eshwarZugz/bart_large-paranmt with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eshwarZugz/bart_large-paranmt") model = AutoModelForSeq2SeqLM.from_pretrained("eshwarZugz/bart_large-paranmt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eshwarZugz/bart_large-paranmt: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/eshwarZugz/bart_large-paranmt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eshwarZugz/bart_large-paranmt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eshwarZugz/bart_large-paranmt/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 6df81f3747fc2eb41e95b50e7f12314df00a5202fb90d59d37fc9b5d775c96a0
- Size of remote file:
- 1.63 GB
- SHA256:
- b4f200d59688d541750b03891d7db5179e89445558826d8abb5b987c65744988
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