Instructions to use WindyTranslate/translate-en-lua with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-en-lua with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="WindyTranslate/translate-en-lua")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-en-lua") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-en-lua", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download target.spm from WindyTranslate/translate-en-lua: direct link, hf CLI and curl.
- Browser
- Download file 823 kB
-
https://huggingface.co/WindyTranslate/translate-en-lua/resolve/main/target.spm
- Command line
-
hf download hf://WindyTranslate/translate-en-lua/target.spm
-
curl -L -o target.spm https://huggingface.co/WindyTranslate/translate-en-lua/resolve/main/target.spm
823 kB
- Xet hash:
- b5d0d0529563623c905b7777aaa173f830667944703bbc07c6f7edaf99cf17bc
- Size of remote file:
- 823 kB
- SHA256:
- ca5c1f582e0347726cb87c0e021ed536a3a4a571119b24f80a8ab83dac7be802
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.