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 last-checkpoint/optimizer.pt from azherali/CodeGenDetect-CodeBert_Lora: direct link, hf CLI and curl.
- Browser
- Download file 7.14 MB
-
https://huggingface.co/azherali/CodeGenDetect-CodeBert_Lora/resolve/8bb02faa3f497a2dbc39c8eee2438fbce9fb76ca/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://azherali/CodeGenDetect-CodeBert_Lora@8bb02faa3f497a2dbc39c8eee2438fbce9fb76ca/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/azherali/CodeGenDetect-CodeBert_Lora/resolve/8bb02faa3f497a2dbc39c8eee2438fbce9fb76ca/last-checkpoint/optimizer.pt
7.14 MB
- Xet hash:
- ae92c7805038daf07dd7b10c854dc81c40d35cd435008535e51bbf827b299ca7
- Size of remote file:
- 7.14 MB
- SHA256:
- 118eb527e3f3d95f6bbf2dc9c4c0579763f722fb5753842653cc84555c706dc2
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