Instructions to use WindyTranslate/translate-lua-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-lua-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="WindyTranslate/translate-lua-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-lua-en") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-lua-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote lora build from WindstormLabs/translate-lua-en, with attribution and change statement
4205b7e verified - Xet hash:
- df899b6fd80f92533cd538242101e39994295e62eb470ed9aef6dde89bc326c6
- Size of remote file:
- 816 kB
- SHA256:
- 645a1e031da47f153ef6fc08972082c912c1c698ea7a9ca941153e7685968c6b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.