Instructions to use hf-tiny-model-private/tiny-random-M2M100ForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-M2M100ForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-M2M100ForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-tiny-model-private/tiny-random-M2M100ForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a534c23776adf76ebaf42b52747aa788087cdf5fd2db76b3ff4b4c7a87fdaaf2
- Size of remote file:
- 8.26 MB
- SHA256:
- 7ac9407a5f75b1813e4278bad4349cb23f8c3261a1018d423184c52a3ce533e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.