Instructions to use Linhz/AlphaEdu_ViT5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linhz/AlphaEdu_ViT5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Linhz/AlphaEdu_ViT5") model = AutoModelForSeq2SeqLM.from_pretrained("Linhz/AlphaEdu_ViT5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d202ddfa11c4622595f2038b614da06f060417d22680f771434779fc2e8b9ddb
- Size of remote file:
- 1.81 GB
- SHA256:
- c00f82011784c0d4c3fb4a61c962bf01dd7ba43e7c3a9890d44d6f5931b355a4
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