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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("prithivida/ALT_CTRLSum") model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/ALT_CTRLSum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights
Joao GanteModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). 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-05.78c7896
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