Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-ff1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-ff1000 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-ff1000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-ff1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-ff1000: direct link, hf CLI and curl.
- Browser
- Download file 192 MB
-
https://huggingface.co/google/t5-efficient-small-ff1000/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-ff1000/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-ff1000/resolve/main/flax_model.msgpack
192 MB
- Xet hash:
- 5a568f281bf2fee46e9ea9708b220c13655206d58efabcee717c09d701e93bb8
- Size of remote file:
- 192 MB
- SHA256:
- ea90dfa6701056aee4edfe1c29dfc448910252883cce53a02d4f6105e282c5bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.