Instructions to use WindyTranslate/translate-pis-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-pis-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-pis-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-pis-en") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-pis-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-pis-en, with attribution and change statement
5d92909 verified - Xet hash:
- 49960ffd48400b50499414ec8dfd57bcd69ec9dc79ea4e5dc6dd5207dbf2820f
- Size of remote file:
- 587 kB
- SHA256:
- 7d5be6006db94fb5647bce0cc064072f318cb70deb49b4a475410f01331557c8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.