Instructions to use Tippawan/mT5-mt-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tippawan/mT5-mt-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Tippawan/mT5-mt-v1") model = AutoModelForSeq2SeqLM.from_pretrained("Tippawan/mT5-mt-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Tippawan/mT5-mt-v1: direct link, hf CLI and curl.
- Browser
- Download file 16.4 MB
-
https://huggingface.co/Tippawan/mT5-mt-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://Tippawan/mT5-mt-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Tippawan/mT5-mt-v1/resolve/main/tokenizer.json
16.4 MB
- Xet hash:
- 6e4511198563026c97d7685c069c6fc602f83635e8033dfddbbcc46aa4c3115e
- Size of remote file:
- 16.4 MB
- SHA256:
- bfad9e2cfe7582477bce420b47a55a5e0bd54e77f69c794560a9f6a910ddaeea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.