Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-dl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-dl2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-dl2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-dl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-dl2: direct link, hf CLI and curl.
- Browser
- Download file 79.1 MB
-
https://huggingface.co/google/t5-efficient-tiny-dl2/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-dl2@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-dl2/resolve/refs%2Fpr%2F1/flax_model.msgpack
79.1 MB
- Xet hash:
- 1edf80ce4b86f7819a8ee99752fd9b5bb93e4cf7b5be356c17f7b381d5b74824
- Size of remote file:
- 79.1 MB
- SHA256:
- 17a3297b7a1765e1ba2cd66fc1c4a3ddaf4d732d92d96fd62b6deb1092a791e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.