Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-small-dm2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-small-dm2000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-small-dm2000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-small-dm2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-small-dm2000: direct link, hf CLI and curl.
- Browser
- Download file 968 MB
-
https://huggingface.co/google/t5-efficient-small-dm2000/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-small-dm2000/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-small-dm2000/resolve/main/flax_model.msgpack
968 MB
- Xet hash:
- 509f2220f646a35b0a023440946fc7c2a1ecd59e80643f5fe7fe2fd4bb606e0f
- Size of remote file:
- 968 MB
- SHA256:
- a19c64f98329e16ba1ff50beb7d5c4aff759cd03264b47c0486726ebd9810950
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