Instructions to use azherali/CodeGenDetect-CodeBert_Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use azherali/CodeGenDetect-CodeBert_Lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/codebert-base") model = PeftModel.from_pretrained(base_model, "azherali/CodeGenDetect-CodeBert_Lora") - Transformers
How to use azherali/CodeGenDetect-CodeBert_Lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("azherali/CodeGenDetect-CodeBert_Lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from azherali/CodeGenDetect-CodeBert_Lora: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/azherali/CodeGenDetect-CodeBert_Lora/resolve/05730b9dbf18cb27a7fbfda3f9f65ae3bbb8be74/training_args.bin
- Command line
-
hf download hf://azherali/CodeGenDetect-CodeBert_Lora@05730b9dbf18cb27a7fbfda3f9f65ae3bbb8be74/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/azherali/CodeGenDetect-CodeBert_Lora/resolve/05730b9dbf18cb27a7fbfda3f9f65ae3bbb8be74/training_args.bin
5.78 kB
- Xet hash:
- a44e00566e5e5f0a05a3a780ff656b582393956e6cbf8ed014437b0afb8504c1
- Size of remote file:
- 5.78 kB
- SHA256:
- 2431b318f5d57feb8902ee5487c8e4d2af146f7f76640e2a0d86b54b6c2eb6a3
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