Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-base-ff6000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-base-ff6000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-base-ff6000") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-base-ff6000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/t5-efficient-base-ff6000: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/google/t5-efficient-base-ff6000/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/t5-efficient-base-ff6000/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/t5-efficient-base-ff6000/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- 345b5ea03e629e2a74f313d0d4cebdd2c5e0288611b52ea461e646166bf0c1d3
- Size of remote file:
- 1.34 GB
- SHA256:
- fab669f9194a85819bf076b82504e62162c16b4bddcc9d37ab37a74cc57eeba7
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