Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-kv128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-kv128 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-kv128") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-kv128", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-large-kv128: direct link, hf CLI and curl.
- Browser
- Download file 4.16 GB
-
https://huggingface.co/google/t5-efficient-large-kv128/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-large-kv128/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-large-kv128/resolve/main/flax_model.msgpack
4.16 GB
- Xet hash:
- 98c670a9e49c2c7dd7e660df026b13dabd59cea3b9bc454a4748b5f95d7329e1
- Size of remote file:
- 4.16 GB
- SHA256:
- 9a831777e10c226ecc68bb5c66ca16d661161f90abb12ed013b7aa7576bdb125
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