Instructions to use WindyTranslate/translate-mg-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-mg-es 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-mg-es")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-mg-es") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-mg-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote herm0 build from WindstormLabs/translate-mg-es, with attribution and change statement
adf85b3 verified - Xet hash:
- 1f043f73111d8a7fdabca7d4860f0c66dc43564f715138eaf8bcfe9e3df29794
- Size of remote file:
- 809 kB
- SHA256:
- fdf78f2a3d116663a48ac25ca813643e980e674bea16213327ee708afb7476ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.