Instructions to use JMDFujitsu/Custom_t5_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JMDFujitsu/Custom_t5_v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JMDFujitsu/Custom_t5_v1") model = AutoModelForSeq2SeqLM.from_pretrained("JMDFujitsu/Custom_t5_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f55bef23794088c04ae02a5482f74b2ea771d64e3e8873032538dbb0b242c71f
- Size of remote file:
- 990 MB
- SHA256:
- c61c2652b30b282563ab3210d3d487461173c622e527d17cb930922317ee7069
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