Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-dl6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-dl6 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-dl6") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-dl6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/t5-efficient-base-dl6: direct link, hf CLI and curl.
- Browser
- Download file 665 MB
-
https://huggingface.co/google/t5-efficient-base-dl6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/t5-efficient-base-dl6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/t5-efficient-base-dl6/resolve/main/pytorch_model.bin
665 MB
- Xet hash:
- 300e226c0198a7ec11def972e01143495d26e4a7c5849fd69ef166803af1446e
- Size of remote file:
- 665 MB
- SHA256:
- 7c80bb9425cb93c514011f3c29e325016b2bae795d1a04e03eaa6456ee132db1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.