metadata
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
pipeline_tag: text-generation
tags:
- code-audit
- security
- cwe
- rocm
- qwen2.5
- amd-hackathon
🔐 Security Auditor Model (14B)
Fine-tuned Qwen2.5-Coder-14B-Instruct khusus untuk analisis kerentanan keamanan kode. Model ini mendeteksi vulnerability, mengklasifikasikan CWE ID, menilai severity, dan memberikan rekomendasi mitigasi terstruktur.
🚀 Quick Load
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "lablab-ai-amd-developer-hackathon/security-auditor-14b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
💬 Example Usage
messages = [
{"role": "user", "content": "Audit this C code for security issues:\n\n<code>\nvoid foo(char* buf, char* input) { strcpy(buf, input); }\n</code>"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=256, temperature=0.2)
print(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))