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
Commit History
-
update model card README.md
3d17c01 -
End of training
84c7872 -
Training in progress, step 6000
abd437c -
Training in progress, step 5500
f028d06 -
Training in progress, step 5000
9ae1c0f -
Training in progress, step 4500
16d1322 -
Training in progress, step 4000
a08fa18 -
Training in progress, step 3500
7535d7f -
Training in progress, step 3000
5b36b0c -
Training in progress, step 2500
feb44ad -
Training in progress, step 2000
f5a62ed -
Training in progress, step 1500
29a8a9d -
Training in progress, step 1000
ee88e50 -
Training in progress, step 500
50bc237 -
Training in progress, step 1500
481619c -
Training in progress, step 1000
65cb825 -
Training in progress, step 500
4a3a7ab -
update model card README.md
c86115d -
End of training
8fef4f8 -
Training in progress, step 3000
d0a2b01 -
Training in progress, step 2500
b595117 -
Training in progress, step 2000
92d5fb9 -
Training in progress, step 1500
c69cee1 -
Training in progress, step 500
b25bfb6 -
initial commit
7ad2687