Instructions to use TIGER-Lab/AceCodeRM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/AceCodeRM-7B with Transformers:
# Load model directly from transformers import AutoTokenizer, Qwen2ForCausalRM tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/AceCodeRM-7B") model = Qwen2ForCausalRM.from_pretrained("TIGER-Lab/AceCodeRM-7B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7030cf2e58dead38199a68a8cd6f6f1a609a6072d7fb38ba5f85b3bb7e21557
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.