Instructions to use WindyTranslate/translate-sv-bem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sv-bem 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-bem")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sv-bem") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sv-bem", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-sv-bem, with attribution and change statement
1c15a83 verified - Xet hash:
- 7b4c64a048a2076f1a2c34fabb0cf6b2024dd736251cba72574ee6c362f51dde
- Size of remote file:
- 855 kB
- SHA256:
- e6ab1aa9be3f1551b0c786e9278d82de2a8c14c6c37ab5b80d38e5b2676a6846
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