Instructions to use sshleifer/tiny-mbart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/tiny-mbart with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/tiny-mbart") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/tiny-mbart") - Notebooks
- Google Colab
- Kaggle
How was this model trained?
#1
by BramVanroy - opened
I'd love to play around with a smaller version of mbart locally for debugging, so this tiny mbart sounds promising! Can you give more details about how this was trained/distilled? Data used, hyperparameters, etc.
Thanks!
at least looking at the demo, it doesn't seem promising
I think I read somewhere that the model was just randomly initialized and not trained at all, but I do not remember whether this occurred to me in a dream or real life.