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:
- 2e4e535336279e6d12fd892b07d85ef3829168fa6605aa9b02aa6d9b6db7c925
- Size of remote file:
- 5.97 kB
- SHA256:
- 770cbf0abbfd3e235d1f468dbddfda750ad68219c958d44fd49e1e9de3aece90
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