Instructions to use ethansimrm/opus_base_ailem_adaptified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethansimrm/opus_base_ailem_adaptified with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ethansimrm/opus_base_ailem_adaptified") model = AutoModelForSeq2SeqLM.from_pretrained("ethansimrm/opus_base_ailem_adaptified", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ethansimrm/opus_base_ailem_adaptified: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/ethansimrm/opus_base_ailem_adaptified/resolve/main/training_args.bin
- Command line
-
hf download hf://ethansimrm/opus_base_ailem_adaptified/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ethansimrm/opus_base_ailem_adaptified/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- 19b9d043d218db670beac126bff2682b7197bdb2343a0065465a81bd7390974d
- Size of remote file:
- 3.77 kB
- SHA256:
- 06045bfca8d45b2ad5c21bc2afdbfa7788b5223a9722cafa595fbbe472e272c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.