Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-dm2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-dm2000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-dm2000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-dm2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-large-dm2000: direct link, hf CLI and curl.
- Browser
- Download file 5.9 GB
-
https://huggingface.co/google/t5-efficient-large-dm2000/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-large-dm2000/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-large-dm2000/resolve/main/flax_model.msgpack
5.9 GB
- Xet hash:
- c36c96d133354c2232d4487f912c8c0ccfbe6e9c2d5a9b8825784b6d6ac08ab1
- Size of remote file:
- 5.9 GB
- SHA256:
- df2d9ef912e89be493c6b0e93bb48c3c08c32d22d374f6a70f16fc0faf52b809
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