Instructions to use WindyTranslate/translate-sv-tw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sv-tw 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-tw")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sv-tw") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sv-tw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-sv-tw, with attribution and change statement
222f55d verified - Xet hash:
- 8f67e3743088184cbdd3f4fd0a4afac5a8a9f189e73be09cf283a457f82d0d93
- Size of remote file:
- 857 kB
- SHA256:
- 17bd3693258b8adb2cc8b08120472fb07c24b7b5da96a11ff28e9980566496f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.