Instructions to use IDEA-CCNL/Randeng-T5-77M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IDEA-CCNL/Randeng-T5-77M with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Randeng-T5-77M") model = AutoModelForSeq2SeqLM.from_pretrained("IDEA-CCNL/Randeng-T5-77M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from IDEA-CCNL/Randeng-T5-77M: direct link, hf CLI and curl.
- Browser
- Download file 155 MB
-
https://huggingface.co/IDEA-CCNL/Randeng-T5-77M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://IDEA-CCNL/Randeng-T5-77M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/IDEA-CCNL/Randeng-T5-77M/resolve/main/pytorch_model.bin
155 MB
- Xet hash:
- ff240c6332136390e6aba282ae6213f44a36815b58e918bf954a381915e24220
- Size of remote file:
- 155 MB
- SHA256:
- 92ce8af84829ce18372bd54b24c499fc0a3691aadda2bea59117c48c06c9d607
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.