Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- Younis2003/secure_dataset_cvefixes
+language:
+- en
+library_name: transformers
+base_model: meta-llama/CodeLlama-13b-hf
+tags:
+- cybersecurity
+- code-security
+- vulnerability-detection
+- secure-code
+- codellama
+- transformers
+---
+
+# CodeLlama_for_code_security
+
+## Overview
+
+CodeLlama_for_code_security is a fine-tuned large language model designed for **vulnerability detection and secure code remediation**.
+
+The model analyzes vulnerable source code and generates structured outputs describing detected vulnerabilities and proposing secure fixes.
+
+This model is built on top of **CodeLlama-13B** and fine-tuned using vulnerability datasets to specialize in secure code analysis tasks.
+
+---
+
+## Intended Use
+
+This model is intended for:
+
+- Secure code analysis  
+- Vulnerability identification  
+- Automatic code remediation suggestions  
+- Security-focused code review assistance  
+- Educational purposes in secure software development  
+
+### Example Use Cases
+
+- Detecting vulnerabilities in open-source projects  
+- Assisting developers in secure coding practices  
+- Research in AI-driven cybersecurity tools  
+
+---
+
+## Training Data
+
+The model was fine-tuned using curated vulnerability datasets including:
+
+- CVE vulnerability descriptions  
+- CWE vulnerability classifications  
+- Code vulnerability datasets  
+- Security patch examples  
+
+Dataset used for fine-tuning:
+
+**secure_dataset_cvefixes**
+
+The dataset focuses on real-world software vulnerabilities and their corresponding secure fixes.
+
+---
+
+## Model Details
+
+Base Model: CodeLlama-13B  
+Architecture: Transformer-based causal language model  
+Fine-tuning Method: Supervised Fine-Tuning (SFT)
+
+The model processes vulnerable code snippets and produces structured outputs that include:
+
+- vulnerability identification
+- vulnerability classification
+- explanation of the vulnerability
+- secure code remediation
+
+---
+
+## Evaluation Results
+
+The model was evaluated using **semantic similarity between generated outputs and ground truth secure fixes**.
+
+Evaluation metric used:
+
+**Embedding Similarity**
+
+| Metric | Score |
+|------|------|
+| Embedding Similarity | **0.9643** |
+
+This corresponds to approximately **96% semantic similarity** between generated remediation outputs and the expected secure fixes.
+
+---
+
+## Example Usage
+
+```python
+from transformers import  AutoModelForCasualLM 
+model_name = "Younis2003/CodeLlama_for_code_security"
+
+model = AutoModelForCasualLM.from_pretrained(model_name , device_map = "auto")
+```
+
+### Limitations
+
+The model may not detect all vulnerabilities.
+
+Results should always be reviewed by a security expert.
+
+The model may generate incorrect fixes in complex systems.
+
+This model is intended as a security assistant, not a replacement for professional security auditing.
+
+### Ethical Considerations
+
+The model is designed for defensive cybersecurity applications.
+It should not be used for malicious activities.
+
+### License
+
+This model follows the Apache 2.0 license and respects the licensing terms of the base model CodeLlama.
+
+### Author
+
+Developed by Younis Alshibli as part of an AI research project focusing on:
+
+AI-driven vulnerability detection
+
+automated secure code remediation
+
+Intelligent security analysis systems