Instructions to use ethansimrm/opus_base_lsp_simple_wce with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethansimrm/opus_base_lsp_simple_wce with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ethansimrm/opus_base_lsp_simple_wce") model = AutoModelForSeq2SeqLM.from_pretrained("ethansimrm/opus_base_lsp_simple_wce", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ethansimrm/opus_base_lsp_simple_wce: direct link, hf CLI and curl.
- Browser
- Download file 597 MB
-
https://huggingface.co/ethansimrm/opus_base_lsp_simple_wce/resolve/main/optimizer.pt
- Command line
-
hf download hf://ethansimrm/opus_base_lsp_simple_wce/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ethansimrm/opus_base_lsp_simple_wce/resolve/main/optimizer.pt
597 MB
- Xet hash:
- 3746a155a2f20f78bdaf0c5eff365d01dd0a54c9652bfaa36797c941be1de77d
- Size of remote file:
- 597 MB
- SHA256:
- d78949a43a5015ccb761b5ade9a80839fa836ac680ab1f7de7a3b9a9228974c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.