Instructions to use IDEA-CCNL/Randeng-T5-784M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IDEA-CCNL/Randeng-T5-784M with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Randeng-T5-784M") model = AutoModelForSeq2SeqLM.from_pretrained("IDEA-CCNL/Randeng-T5-784M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82b85cb8e7e58b4b7052a0515d2e715d9aab72ac43e03b409ae91cea58ab68ce
- Size of remote file:
- 1.57 GB
- SHA256:
- 26c2fa8fbe0313050218aec11f96900b25b3839fbf383d23d45fa0a8c7a74b7c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.