Instructions to use stas/tiny-wmt19-en-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stas/tiny-wmt19-en-ru with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("stas/tiny-wmt19-en-ru") model = AutoModelForSeq2SeqLM.from_pretrained("stas/tiny-wmt19-en-ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stas/tiny-wmt19-en-ru: direct link, hf CLI and curl.
- Browser
- Download file 30.8 kB
-
https://huggingface.co/stas/tiny-wmt19-en-ru/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stas/tiny-wmt19-en-ru/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stas/tiny-wmt19-en-ru/resolve/main/pytorch_model.bin
30.8 kB
- Xet hash:
- 6a2782e36b73fa0f282100722867e39c26da94c9da3bdb2e23fdde00d18231a4
- Size of remote file:
- 30.8 kB
- SHA256:
- 2305fa9d6c7c3b143721dbfa6d0cf93968280e283c5677e0e48cb87779b30418
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.