Instructions to use WindyTranslate/translate-wls-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-wls-en 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-wls-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-wls-en") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-wls-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-wls-en, with attribution and change statement
53172d8 verified - Xet hash:
- 1efcf17e657a367bbf1143dfe6ad379787ba53599f4d7073dc52273b7e0c77d9
- Size of remote file:
- 464 kB
- SHA256:
- 0752769b9132b7174a37bb100a75f2921d2ea12dc39afd38c00379a9c14fb809
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.