Instructions to use google/umt5-xxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/umt5-xxl with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("google/umt5-xxl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from google/umt5-xxl: direct link, hf CLI and curl.
- Browser
- Download file 16.9 MB
-
https://huggingface.co/google/umt5-xxl/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://google/umt5-xxl@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/google/umt5-xxl/resolve/refs%2Fpr%2F2/tokenizer.json
16.9 MB
- Xet hash:
- 47672a89fa898aee2784f3bc6bac9c12453dca6cb88ba3fc3b0f54f21580406e
- Size of remote file:
- 16.9 MB
- SHA256:
- af904105ce1071b1202bba0059a841f4a7b85b48b6ec179c4948e3483476e0dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.