Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Vrushali/model-t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vrushali/model-t5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vrushali/model-t5") model = AutoModelForSeq2SeqLM.from_pretrained("Vrushali/model-t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 62d67ac2bde3fc5e776788aa0d21ae0731c6c464a173b002ae49884dbc96a5df
- Size of remote file:
- 990 MB
- SHA256:
- 4ea3b6272f433cece7c35c89c10ee4ac31097c15045439d767cf804174e7aacb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.