Instructions to use azherali/CodeGenDetect-Unixcoder_Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use azherali/CodeGenDetect-Unixcoder_Lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/unixcoder-base") model = PeftModel.from_pretrained(base_model, "azherali/CodeGenDetect-Unixcoder_Lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b51b8df8598478d28aa60f1ea0421477bbb98043a462967912b4b5cde3200bce
- Size of remote file:
- 3.56 MB
- SHA256:
- 5aca8141edb0d3ae52c87d3b6e7d49b62d88848b6c9e53b52a63e02409cc9043
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.