Instructions to use WindyTranslate/translate-sv-efi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sv-efi 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-efi")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sv-efi") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sv-efi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-sv-efi, with attribution and change statement
93ce970 verified - Xet hash:
- 448f40e5769e12e1a3ccdd230a2e588c95b03883c28594f1e77eb1592f16f1e7
- Size of remote file:
- 856 kB
- SHA256:
- 728a00c17f50a2ff1971e188bc643cd7243f697998d1e217dd9bcbfa8fb9bc33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.