Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
t5
text2text-generation
seq2seq
lm-head
text-generation-inference
Instructions to use gsarti/it5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/it5-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-base") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from gsarti/it5-base: direct link, hf CLI and curl.
- Browser
- Download file 990 MB
-
https://huggingface.co/gsarti/it5-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://gsarti/it5-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/gsarti/it5-base/resolve/main/flax_model.msgpack
990 MB
- Xet hash:
- 3c6e83590fbb3d897bdd816c0d0fcbba46387237a871847a60a6162061e45aaf
- Size of remote file:
- 990 MB
- SHA256:
- 07bc3e5372fa4ba92ae03a93850a5f4e1811ec8824f16a218b62177cc5e3f65b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.