Translation
Transformers
PyTorch
TensorFlow
JAX
Safetensors
t5
text2text-generation
summarization
text-generation-inference
Instructions to use google-t5/t5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-t5/t5-large with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="google-t5/t5-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-large") model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google-t5/t5-large: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/google-t5/t5-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google-t5/t5-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-t5/t5-large/resolve/main/flax_model.msgpack
2.95 GB
- Xet hash:
- c84b0a1e50ef0680700a2d7c95ca498217e8a5304daed104fa0b7139f9d4ee10
- Size of remote file:
- 2.95 GB
- SHA256:
- e58141eef80c2bddce2c9fa33c3fd0bd537a1094e865d54f28eaab5d9fe3f94d
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