Instructions to use dtorber/NAS-bilingue-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/NAS-bilingue-final with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/NAS-bilingue-final") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/NAS-bilingue-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dtorber/NAS-bilingue-final: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/dtorber/NAS-bilingue-final/resolve/main/training_args.bin
- Command line
-
hf download hf://dtorber/NAS-bilingue-final/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dtorber/NAS-bilingue-final/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 493ffa98cc547647d3ecea86877550b55760326c3bfeb0fac0b95c96da53ae82
- Size of remote file:
- 3.64 kB
- SHA256:
- 81e3b61835cb9e885736d6b9d47f384fbb2e248cd8a93217b0f801e836862e29
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.