Instructions to use Evuv/mt5-base-torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evuv/mt5-base-torch with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Evuv/mt5-base-torch") model = AutoModelForSeq2SeqLM.from_pretrained("Evuv/mt5-base-torch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Evuv/mt5-base-torch: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/Evuv/mt5-base-torch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Evuv/mt5-base-torch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Evuv/mt5-base-torch/resolve/main/pytorch_model.bin
2.33 GB
- Xet hash:
- 1d705b1420d99b9b5bb0ba90ee8802a4da1cd541d0c2133e85a10c4584987295
- Size of remote file:
- 2.33 GB
- SHA256:
- 29094233c75b06e2ccb8760e1c764ef00aeff9fed816adfb7dd6f588b831ecfc
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