Transformers
PyTorch
TensorFlow
JAX
English
t5
text2text-generation
deep-narrow
text-generation-inference
Instructions to use google/t5-efficient-large-kv32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5-efficient-large-kv32 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5-efficient-large-kv32") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-efficient-large-kv32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c958de7d96e2a57836c4214bef8b18cd3f66ce88338d0001a1ddb198530d017
- Size of remote file:
- 2.35 GB
- SHA256:
- 53fbfdf2d2cc97b0fd48735a9e88fbf03e39d9e7c1b1fdf37484487062ee299a
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