Translation
Transformers
PyTorch
TensorFlow
JAX
Rust
ONNX
Safetensors
t5
text2text-generation
summarization
text-generation-inference
Instructions to use google-t5/t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-t5/t5-small 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-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download onnx/decoder_with_past_model.onnx from google-t5/t5-small: direct link, hf CLI and curl.
- Browser
- Download file 220 MB
-
https://huggingface.co/google-t5/t5-small/resolve/main/onnx/decoder_with_past_model.onnx
- Command line
-
hf download hf://google-t5/t5-small/onnx/decoder_with_past_model.onnx
-
curl -L -o decoder_with_past_model.onnx https://huggingface.co/google-t5/t5-small/resolve/main/onnx/decoder_with_past_model.onnx
220 MB
- Xet hash:
- 8f7a3c087c098e2892292374b8dcf8104a8528b9e1f6978763ca20d89bfe8123
- Size of remote file:
- 220 MB
- SHA256:
- 2eb7790f56c47e6498e850041a4c9c60ebc8133d3792f30d91eebd242e819836
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.