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poonia98
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Model Details
Model Provider
poonia98
Model Tree
Qwen/Qwen2.5-Coder-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_model_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct"adapter_id = "poonia98/authguard-1.5b"tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)base_model = AutoModelForCausalLM.from_pretrained(base_model_id,torch_dtype=torch.float16,device_map="auto",trust_remote_code=True)model = PeftModel.from_pretrained(base_model, adapter_id)code_snippet = '''@app.get("/api/user/{user_id}/documents/{doc_id}")def get_doc(user_id: int, doc_id: int, db: Session = Depends(get_db)):# Vulnerability: Direct query without verifying tenant_id or user ownershipreturn db.query(Document).filter(Document.id == doc_id).first()'''prompt = f"<|im_start|>system\\nYou are an expert security auditor specialized in web application authentication and authorization vulnerabilities.\\nOutput valid JSON.\\n<|im_end|>\\n<|im_start|>user\\nLanguage: python\\n\\nCode:\\n```python\\n{code_snippet}\\n```<|im_end|>\\n<|im_start|>assistant\\n"inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))