Instructions to use eunyounglee/mBART_tokenizer_custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunyounglee/mBART_tokenizer_custom with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eunyounglee/mBART_tokenizer_custom") model = AutoModelForSeq2SeqLM.from_pretrained("eunyounglee/mBART_tokenizer_custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eunyounglee/mBART_tokenizer_custom: direct link, hf CLI and curl.
- Browser
- Download file 2.44 GB
-
https://huggingface.co/eunyounglee/mBART_tokenizer_custom/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eunyounglee/mBART_tokenizer_custom/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eunyounglee/mBART_tokenizer_custom/resolve/main/pytorch_model.bin
2.44 GB
- Xet hash:
- ca677a4c8f91b36fb953b83cde70dde261902c0f3fb7621fc89b407bc4869183
- Size of remote file:
- 2.44 GB
- SHA256:
- cd9251f068510861b2db3fc21b27b7135d64fd3fb5a5b6d7048be3d6803b5446
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