elsiddik
/

finsec_detector

@@ -16,38 +16,38 @@ language:
 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.
 ---
-## 🏆 Verified Benchmark Performance
-Evaluating **FineSec-Detector** on multi-language vulnerability benchmarks (SQLi, RCE, XSS, Path Traversal, Deserialization, Buffer Overflows) yielded outstanding rating scores:
 | Metric | Score | Rating | Analysis |
 |---|---|---|---|
-| **🎯 Precision Rate** | **100.0%** | 🌟 Perfect | **Zero False Positives!** Safe code is never misflagged. |
-| **🔎 Detection Recall** | **83.3%** | 🚀 High | High-confidence detection across Python, C, JS, and Go. |
-| **⚖️ F1 Rating Score** | **90.9%** | 🏆 Outstanding | Superior overall vulnerability detection balance. |
 ---
-## ✨ 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
@@ -85,7 +85,7 @@ print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_toke
 ---
-## 📊 Sample Output (Structured JSON)
 ```json
 {
@@ -102,20 +102,20 @@ print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_toke
 ---
-## ⚙️ 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.

 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. It audits source code, identifies vulnerabilities, classifies severity and CWE IDs, and produces ready-to-merge secure code patches in structured JSON.
 ---
+## Verified Benchmark Performance
+Evaluating **FineSec-Detector** on multi-language vulnerability benchmarks (SQL Injection, RCE, XSS, Path Traversal, Insecure Deserialization, Buffer Overflows) yielded the following performance metrics:
 | Metric | Score | Rating | Analysis |
 |---|---|---|---|
+| Precision Rate | 100.0% | Perfect | Zero false positives. Safe code is never misflagged. |
+| Detection Recall | 83.3% | High | High-confidence detection across Python, C, JS, and Go. |
+| F1 Rating Score | 90.9% | Outstanding | Superior overall vulnerability detection balance. |
 ---
+## 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 and 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 and Patching: Generates diffs and secure code refactors directly replacing vulnerable logic.
 ---
+## Quickstart: Inference
+### 1. Using Unsloth (Fast and Memory Efficient)
 ```python
 from unsloth import FastLanguageModel
 ---
+## Sample Output (Structured JSON)
 ```json
 {
 ---
+## 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 and 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.