Instructions to use eunyounglee/mBART_data_221115 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunyounglee/mBART_data_221115 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("eunyounglee/mBART_data_221115") model = AutoModelForSeq2SeqLM.from_pretrained("eunyounglee/mBART_data_221115", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eunyounglee/mBART_data_221115: direct link, hf CLI and curl.
- Browser
- Download file 3.2 kB
-
https://huggingface.co/eunyounglee/mBART_data_221115/resolve/main/training_args.bin
- Command line
-
hf download hf://eunyounglee/mBART_data_221115/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eunyounglee/mBART_data_221115/resolve/main/training_args.bin
3.2 kB
- Xet hash:
- d3fe769741b54782df793540728395c10460631b8c6f19f0b9238400df6ca02c
- Size of remote file:
- 3.2 kB
- SHA256:
- 17bd0431fff19a2651e362543069188af49380e108a731e9f789a638407dfd7e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.