Instructions to use Shivam098/opt-Translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shivam098/opt-Translator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shivam098/opt-Translator") model = AutoModelForSeq2SeqLM.from_pretrained("Shivam098/opt-Translator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0f5044471418db743b7f7fd40fe4239ecec4e9a9bc450a7d4268cc01354ab73
- Size of remote file:
- 1.95 GB
- SHA256:
- af0d709e67a01d0d4e29d62aced7348da5890b7b8df7bb71ab717cda6f7786a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.