Younis2003
/

CodeLlama_for_code_security

@@ -2,206 +2,163 @@
 base_model: meta-llama/CodeLlama-13b-hf
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:meta-llama/CodeLlama-13b-hf
 - lora
-- transformers
 ---
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-### Framework versions
-- PEFT 0.18.1

 base_model: meta-llama/CodeLlama-13b-hf
 library_name: peft
 pipeline_tag: text-generation
+language:
+- en
+license: apache-2.0
+datasets:
+- Younis2003/secure_dataset_cvefixes
 tags:
+- cybersecurity
+- vulnerability-detection
+- secure-code
+- codellama
 - lora
+- peft
 ---
+# CodeLlama_for_code_security
+## Overview
+CodeLlama_for_code_security is a **LoRA fine-tuned adapter** designed for vulnerability detection and secure code remediation.
+The model analyzes vulnerable source code and generates a secure fixed version together with structured vulnerability explanations including CVE and CWE metadata.
+The adapter is trained on top of **CodeLlama-13B**.
+---
+# Model Details
+**Developed by:** Younis Alshibli
+**Model type:** LoRA Adapter (PEFT)
+**Base Model:** CodeLlama-13B
+**Language:** English
+**License:** Apache 2.0
+---
+# Intended Use
+The model is designed for:
+- Vulnerability detection
+- Secure code remediation
+- Security analysis of source code
+- Automated security review
+- AI-assisted cybersecurity research
+Example applications:
+- Secure code assistants
+- AI vulnerability scanners
+- Cybersecurity research tools
+---
+# Evaluation
+The model was evaluated using **semantic similarity between generated fixes and ground truth secure fixes**.
+| Metric | Score |
+|------|------|
+| Embedding Similarity | **0.9643** |
+This corresponds to approximately **96% semantic similarity** between predicted outputs and expected secure code fixes.
+---
+# Prompt Format
+The model expects prompts in the following structured format:
+```
+### System:
+You are a secure code remediation and vulnerability analysis engine.
+### Programming Language:
+<language>
+### Vulnerable Code:
+<code>
+```
+The model will generate:
+* Fixed Code
+* Vulnerability Explanation
+* CVE metadata
+* CWE metadata
+---
+# How to Use
+This model is a **LoRA adapter** and requires the base model.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base_model = "meta-llama/CodeLlama-13b-hf"
+adapter = "Younis2003/CodeLlama_for_code_security"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(model, adapter)
+```
+---
+# Training Data
+The model was trained using the dataset:
+**secure_dataset_cvefixes**
+Dataset source:
+```
+Younis2003/secure_dataset_cvefixes
+```
+The dataset contains:
+* vulnerable code
+* fixed code
+* CVE descriptions
+* CWE classifications
+---
+# Limitations
+* The model may not detect all vulnerabilities.
+* Results should always be reviewed by security experts.
+* Complex security flaws may require manual analysis.
+---
+# Ethical Considerations
+This model is intended for **defensive cybersecurity research and secure software development**.
+It should not be used for malicious activities.
+---
+# Author
+Developed by **Younis Alshibli** as part of an AI research project on:
+* AI vulnerability detection
+* automated secure code remediation