Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-kv32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-kv32 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-kv32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-kv32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-base-kv32: direct link, hf CLI and curl.
- Browser
- Download file 722 MB
-
https://huggingface.co/google/t5-efficient-base-kv32/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-base-kv32/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-base-kv32/resolve/main/flax_model.msgpack
722 MB
- Xet hash:
- 05bc1fd58593a28404c4096856b2b043f11535e2bee4845a6c2918d3c162da35
- Size of remote file:
- 722 MB
- SHA256:
- 7a4cfd2669b1692cb1afa185a2edf1952c4643866f96484be6dd8912f4d0c72d
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