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 pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://google-t5/t5-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google-t5/t5-large/resolve/main/pytorch_model.bin
2.95 GB
- Xet hash:
- 4ce0fa120f8dbe8d50822c78d2fa82c2f2f4f28851fed1b91f9d2acbd56e0aee
- Size of remote file:
- 2.95 GB
- SHA256:
- c4d6bbca0f16985807fbe5089ac5be9643e4493c2b8a1772a6e15de1219989ba
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