Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-el4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-el4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-el4") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-el4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-large-el4: direct link, hf CLI and curl.
- Browser
- Download file 1.94 GB
-
https://huggingface.co/google/t5-efficient-large-el4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-large-el4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-large-el4/resolve/main/flax_model.msgpack
1.94 GB
- Xet hash:
- b411f913d964c47ce9d58f87c3c84915bfdbdc5caf29d92d407b9523fde79b1c
- Size of remote file:
- 1.94 GB
- SHA256:
- 8094369d3d5699d2693036878eac3ffd8d6730f89a6c531de39ddecfbe5fc452
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