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README.md

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 ---
-license: mit
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+library_name: unsloth
+tags:
+- cybersecurity
+- vulnerability-detection
+- cve
+- code-audit
+- code-repair
+- qwen2.5-coder
+- fine-sec
+datasets:
+- custom-sec-cve
+language:
+- en
+- code
+pipeline_tag: text-generation
 ---
+
+# 🛡️ FineSec-Detector: Specialized Security LLM (Qwen2.5-Coder-7B-Instruct)
+
+**FineSec-Detector** is a 7B parameter specialized cybersecurity Large Language Model fine-tuned on high-precision CVE vulnerability reports, real-world exploit benchmarks, and secure code repair patterns using **Unsloth 4-bit QLoRA**.
+
+The model acts as an automated Senior Application Security (AppSec) Auditor & Penetration Tester. It audits source code, identifies vulnerabilities, classifies severity & CWE IDs, and produces ready-to-merge secure code patches in structured JSON.
+
+---
+
+## ✨ Key Features
+
+- 🔍 **Automated Vulnerability Detection**: Audits Python, C/C++, JavaScript, Go, PHP, Java, and Bash source code.
+- 🎯 **Structured JSON Output**: Produces standardized security reports suitable for CI/CD pipeline integration.
+- 🏷️ **CWE & Severity Classification**: Classifies bugs into standard CWE categories (e.g., CWE-89 SQLi, CWE-79 XSS, CWE-78 RCE, CWE-120 Buffer Overflow) with CVSS-aligned severity levels (`CRITICAL`, `HIGH`, `MEDIUM`, `LOW`).
+- 🛠️ **Remediation & Patching**: Generates diffs and secure code refactors directly replacing vulnerable logic.
+
+---
+
+## 🚀 Quickstart: Inference
+
+### 1. Using Unsloth (Fastest & Memory Efficient)
+
+```python
+from unsloth import FastLanguageModel
+
+# Load model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "elsiddik/finsec_detector",
+    max_seq_length = 1024,
+    load_in_4bit = True,
+)
+FastLanguageModel.for_inference(model)
+
+# Security audit prompt
+prompt = """### System Prompt:
+You are FineSec-AI, an expert Application Security Engineer. Analyze code snippet for vulnerabilities and output JSON report with fields: 'vulnerabilities' (list of objects with severity, cwe, description, vulnerable_line, fix_code).
+
+### Input Code:
+```python
+import sqlite3
+
+def login(username, password):
+    conn = sqlite3.connect('users.db')
+    cursor = conn.cursor()
+    query = f"SELECT * FROM users WHERE username = '{username}' AND password = '{password}'"
+    cursor.execute(query)
+    return cursor.fetchone()
+```
+
+### Security Analysis (JSON):"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+## 📊 Sample Output (Structured JSON)
+
+```json
+{
+  "is_vulnerable": true,
+  "severity": "CRITICAL",
+  "cwe": "CWE-89",
+  "vulnerability_type": "SQL Injection",
+  "description": "User input is directly concatenated into the SQL query string without parameterization, allowing unauthenticated SQL injection.",
+  "vulnerable_code": "query = f\"SELECT * FROM users WHERE username = '{username}' AND password = '{password}'\"",
+  "remediation": "Use parameterized SQL queries with placeholder parameters.",
+  "fixed_code": "query = 'SELECT * FROM users WHERE username = ? AND password = ?'\ncursor.execute(query, (username, password))"
+}
+```
+
+---
+
+## ⚙️ Model Details
+
+| Attribute | Details |
+|---|---|
+| **Base Architecture** | Qwen2.5-Coder-7B-Instruct |
+| **Fine-Tuning Method** | QLoRA 4-bit (Unsloth) |
+| **LoRA Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
+| **LoRA Rank (r)** | 16 |
+| **LoRA Alpha** | 32 |
+| **Context Window** | 1024 tokens |
+| **License** | Apache-2.0 |
+
+---
+
+## 🔒 Intended Use & Disclaimer
+
+**FineSec-Detector** is designed for defensive security purposes, code auditing, secure code development, and AppSec integration. Users are responsible for exercising due diligence when integrating model output into production systems.