Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-tiny-ff6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-tiny-ff6000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-tiny-ff6000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-tiny-ff6000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-tiny-ff6000: direct link, hf CLI and curl.
- Browser
- Download file 146 MB
-
https://huggingface.co/google/t5-efficient-tiny-ff6000/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-tiny-ff6000@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-tiny-ff6000/resolve/refs%2Fpr%2F1/flax_model.msgpack
146 MB
- Xet hash:
- 80822f50f7a700e770e9014d6d58ece4bbbfe222301d16bf209006a3c702c86d
- Size of remote file:
- 146 MB
- SHA256:
- 8e8f436818fa2575a879f2120f2f30b686e7ab044b92d15bcd8e29da6cdb2853
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.