Instructions to use ethansimrm/opus_base_wce_adaptified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethansimrm/opus_base_wce_adaptified with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ethansimrm/opus_base_wce_adaptified") model = AutoModelForSeq2SeqLM.from_pretrained("ethansimrm/opus_base_wce_adaptified", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ethansimrm/opus_base_wce_adaptified: direct link, hf CLI and curl.
- Browser
- Download file 299 MB
-
https://huggingface.co/ethansimrm/opus_base_wce_adaptified/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ethansimrm/opus_base_wce_adaptified/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ethansimrm/opus_base_wce_adaptified/resolve/main/pytorch_model.bin
299 MB
- Xet hash:
- cd9e03968a9124dd361e9d295b1ca5eceff7268f7a440741615081dfe0d63218
- Size of remote file:
- 299 MB
- SHA256:
- 0e09dfdff447fd35dd7560e343c5f5184855dcb9fa967131802b070af33b46a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.