Instructions to use stas/tiny-m2m_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stas/tiny-m2m_100 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("stas/tiny-m2m_100") model = AutoModelForSeq2SeqLM.from_pretrained("stas/tiny-m2m_100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stas/tiny-m2m_100: direct link, hf CLI and curl.
- Browser
- Download file 4.14 MB
-
https://huggingface.co/stas/tiny-m2m_100/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stas/tiny-m2m_100/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stas/tiny-m2m_100/resolve/main/pytorch_model.bin
4.14 MB
- Xet hash:
- eef09ac947e5dbf6aac967647d6a7d308f225f5288ca16e19996bbf830e7cab7
- Size of remote file:
- 4.14 MB
- SHA256:
- 62e77e2232830ad7d9bcf9bdbd1d447aa0bdaa3e0ed794b4ebda0d094c95e49e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.