Instructions to use ethansimrm/opus_base_ailem_random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethansimrm/opus_base_ailem_random with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ethansimrm/opus_base_ailem_random") model = AutoModelForSeq2SeqLM.from_pretrained("ethansimrm/opus_base_ailem_random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ethansimrm/opus_base_ailem_random: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/ethansimrm/opus_base_ailem_random/resolve/main/training_args.bin
- Command line
-
hf download hf://ethansimrm/opus_base_ailem_random/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ethansimrm/opus_base_ailem_random/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- 8dcc9441805d79f07e697c1fbecea146eb1c49a652b3a02203256eab048a28e5
- Size of remote file:
- 3.77 kB
- SHA256:
- 4e6c61042e7a998c0fecc8637c8678e4c211809e57265d0edb7e047a99b13a43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.