Instructions to use psxjp5/mt5-small_25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use psxjp5/mt5-small_25 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("psxjp5/mt5-small_25") model = AutoModelForSeq2SeqLM.from_pretrained("psxjp5/mt5-small_25", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from psxjp5/mt5-small_25: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/psxjp5/mt5-small_25/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://psxjp5/mt5-small_25/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/psxjp5/mt5-small_25/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- f4e521d9a1fd858bcc2b259bb2709fa0d79db03c1e49eeec5828d67d4a6a2d8b
- Size of remote file:
- 1.2 GB
- SHA256:
- 0273c9ce04ee43217b32f140ff0966013ef21c2aef8ecbfb4df094d3f35ba2f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.