Instructions to use psxjp5/mt5-small_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use psxjp5/mt5-small_new with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("psxjp5/mt5-small_new") model = AutoModelForSeq2SeqLM.from_pretrained("psxjp5/mt5-small_new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from psxjp5/mt5-small_new: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/psxjp5/mt5-small_new/resolve/main/training_args.bin
- Command line
-
hf download hf://psxjp5/mt5-small_new/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/psxjp5/mt5-small_new/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- e9920db62913af279762e2a396c17ac064cd46dc4d2355e34943dc22dbad6a65
- Size of remote file:
- 4.09 kB
- SHA256:
- 9a6bb4ddad516b74d5c67ee0183817665b542e565cc27b2b8c5d28a3ab7f4783
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.