Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff2000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff2000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-ff2000: direct link, hf CLI and curl.
- Browser
- Download file 62.3 MB
-
https://huggingface.co/google/t5-efficient-tiny-ff2000/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-ff2000@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-ff2000/resolve/refs%2Fpr%2F1/flax_model.msgpack
62.3 MB
- Xet hash:
- 3fd4f0610923d6d3d526caabafe0a3c29ae589161000c554671cd00903c3cd37
- Size of remote file:
- 62.3 MB
- SHA256:
- b4dc868fc99c498610266afad81cc4383ce3f7a274d2c5d53c359740a750e882
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