Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-dl6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-dl6 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-dl6") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-dl6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-dl6: direct link, hf CLI and curl.
- Browser
- Download file 95.9 MB
-
https://huggingface.co/google/t5-efficient-tiny-dl6/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-dl6@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-dl6/resolve/refs%2Fpr%2F1/flax_model.msgpack
95.9 MB
- Xet hash:
- 66c39e501fd27716a2c8e921f9cebf5b03b0dd9772f2b140af3550df76562080
- Size of remote file:
- 95.9 MB
- SHA256:
- e9f852110eddbbdecac4aed32034740edb7b153462894b4776562c3e264658ef
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