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 training_args.bin from Evuv/mt5-base-torch: direct link, hf CLI and curl.
- Browser
- Download file 3.71 kB
-
https://huggingface.co/Evuv/mt5-base-torch/resolve/main/training_args.bin
- Command line
-
hf download hf://Evuv/mt5-base-torch/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Evuv/mt5-base-torch/resolve/main/training_args.bin
3.71 kB
- Xet hash:
- f0538c41def525ba27d56d939ccad34c3ee038bbc739798b2656dd491d44982e
- Size of remote file:
- 3.71 kB
- SHA256:
- f301ab711b3cecbc9fb03fee2b1bf408eff63626d0b778091a213bb22aa8b82b
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