Instructions to use lmz/candle-quantized-t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmz/candle-quantized-t5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lmz/candle-quantized-t5") model = AutoModelForSeq2SeqLM.from_pretrained("lmz/candle-quantized-t5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 331e953bc4f015bdf791ed879b27c149267f3de3ed40816ead2cfa0289116cee
- Size of remote file:
- 263 MB
- SHA256:
- 493a8e9f31338409e4ebd1a399235eff0e6e51176efc2ce1e7003b5c9ce850c3
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