Instructions to use SummerSigh/mt0-small-ROT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SummerSigh/mt0-small-ROT with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SummerSigh/mt0-small-ROT") model = AutoModelForSeq2SeqLM.from_pretrained("SummerSigh/mt0-small-ROT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SummerSigh/mt0-small-ROT: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/SummerSigh/mt0-small-ROT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SummerSigh/mt0-small-ROT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SummerSigh/mt0-small-ROT/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 113677a6e5064b20577b1bf06f96effa0892b907a9904e4058008650bb3cd748
- Size of remote file:
- 1.2 GB
- SHA256:
- b67b2d85f2e6e734b3eb14395833a936cd0abd85724c06ac589a916334cba3f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.