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:
- 80d391350f456acc6badd1462efcd5176cef6eac3daf8049f17d93087f6c38d0
- Size of remote file:
- 8.24 MB
- SHA256:
- a83071b176c6c5210fab4c4e1470441a9d625c4a828cca0f19f55ddc348a0017
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.