Instructions to use WindyTranslate/translate-sv-mfe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sv-mfe 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-mfe")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sv-mfe") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sv-mfe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-sv-mfe, with attribution and change statement
133f5f2 verified - Xet hash:
- 415a91bdec8aa7f486d912545cd19931d45ecfdfe504f74a7ddd30fb099da291
- Size of remote file:
- 836 kB
- SHA256:
- e20bbef0942b9f931f25f4cf58c61538ee190cf06208e33fee53635bc8df1616
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