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:
- dc676f8520c1e90c6e46731b74240f489a6db511576c6ac42d6d2dfc1a23bb11
- Size of remote file:
- 64.4 MB
- SHA256:
- 30b202732ece72a7c4e8bc5875c800ef322d4fa2ae3cde1051c444a339303ef1
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