Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff3000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff3000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff3000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 17d60a6c65ffa3da279ffcf5aa87271228f134963a25ceee234d281ac30271dd
- Size of remote file:
- 95.8 MB
- SHA256:
- 8727318d1ee686a45e39ac1c829af1ba97ded36d443902bdea859fd6c2ad73d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.