Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-nh1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-nh1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-nh1") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-nh1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-nh1: direct link, hf CLI and curl.
- Browser
- Download file 52.8 MB
-
https://huggingface.co/google/t5-efficient-tiny-nh1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-nh1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-nh1/resolve/main/flax_model.msgpack
52.8 MB
- Xet hash:
- 2ecd26c8ad317436906d0308547b53730be629d807ced4f6bd57fd36b4a6d316
- Size of remote file:
- 52.8 MB
- SHA256:
- e1a363ac4ac99012f87bee2933ffd24b73906814c1f6f1f6c9ae405c4bd3e5ce
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