Instructions to use FiveC/ViTay-TDA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FiveC/ViTay-TDA with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("FiveC/ViTay-TDA") model = AutoModelForSeq2SeqLM.from_pretrained("FiveC/ViTay-TDA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 343caebcdcbdbac1e889acfaa250ef53aeb3cf67f3ac860bb2bb07d5fc3cde8e
- Size of remote file:
- 1.58 GB
- SHA256:
- 6d81e77202a7ca2ec09a58a71bd18e4cf35267838e94c9de7ed4e4c048536f1d
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