Instructions to use CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-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="CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En") model = AutoModelForMultimodalLM.from_pretrained("CadenzaBaron/M2M100-418M-for-GameTranslation-Finetuned-Zh-En") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecc51c6121610da5a43bfe3e14863ad36b2f708bd6640fae873197df410ac5a4
- Size of remote file:
- 4.54 kB
- SHA256:
- 1b4ee63fcb956cb6cef0772e37f1c4c73a3705e0dae4360a4d2b1837a1c8b3d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.