Instructions to use WindyTranslate/translate-sv-to with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sv-to 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-sv-to")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sv-to") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sv-to", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-sv-to, with attribution and change statement
095b329 verified - Xet hash:
- 5d898e6573b8156ff4ecf14955fa4132f529c0c7dbcdfdad6372a6186bc3ffe9
- Size of remote file:
- 855 kB
- SHA256:
- 9472bb3d81d14819ab1251999a72adda060246a08b25a084b2fb03f8e705b335
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.