Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-kv256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-kv256 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-kv256") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-kv256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-kv256: direct link, hf CLI and curl.
- Browser
- Download file 469 MB
-
https://huggingface.co/google/t5-efficient-small-kv256/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-kv256/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-kv256/resolve/main/flax_model.msgpack
469 MB
- Xet hash:
- b46de406921431180bed4f0a1b3a04d83c4a049398fadcc543f7a96a722cb9ed
- Size of remote file:
- 469 MB
- SHA256:
- 52a35de0a3aa5e68903f7879e548436cd2f818e8cdedabc72df51f18b5656a26
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