Instructions to use prithivida/ALT_CTRLSum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivida/ALT_CTRLSum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("prithivida/ALT_CTRLSum") model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/ALT_CTRLSum") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#1
by joaogante - opened
Model converted by the transformers' pt_to_tf CLI.
All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=4.959e-05; Maximum converted output difference=4.959e-05.
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prithivida changed pull request status to merged