Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-ff3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-ff3000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-ff3000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-ff3000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-ff3000: direct link, hf CLI and curl.
- Browser
- Download file 292 MB
-
https://huggingface.co/google/t5-efficient-small-ff3000/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-ff3000/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-ff3000/resolve/main/flax_model.msgpack
292 MB
- Xet hash:
- 0c51c6175da831217a28ce2aa1eed3f03b5b8512b549608d2e3136023044a857
- Size of remote file:
- 292 MB
- SHA256:
- 1ff23753284319f318ff941f06c033bddcce2f9b10b25158d3d6ca7bf63be1f5
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