Instructions to use WindyTranslate/translate-sk-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sk-fr 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-sk-fr")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sk-fr") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sk-fr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-sk-fr, with attribution and change statement
a7be4ed verified - Xet hash:
- 9f2f930d627ae28470615b4763c8ff56b843293e0dbf4301f52caec3285e8210
- Size of remote file:
- 827 kB
- SHA256:
- 606b5d07e3cb0029b8dcabcf53b03b2f3da07cea71dec0fdd9386b030bd2ac7c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.