Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
t5
text2text-generation
seq2seq
lm-head
text-generation-inference
Instructions to use gsarti/it5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/it5-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-small") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from gsarti/it5-small: direct link, hf CLI and curl.
- Browser
- Download file 308 MB
-
https://huggingface.co/gsarti/it5-small/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://gsarti/it5-small/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/gsarti/it5-small/resolve/main/flax_model.msgpack
308 MB
- Xet hash:
- c9f2ff69ba0ab6e4b371668220062d501aac242968f475086c9c453bb9cf243f
- Size of remote file:
- 308 MB
- SHA256:
- 3569f419b9c302aabbebda4449804226bd4f234c2748e6af0f18c42365252bf2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.