Create README.md

README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+tags:
+- cybersecurity
+- chatml
+---
+
+<div align="center">
+  <img src="https://i.imgur.com/nSEPNYW.png" alt="Serpe-7B" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
+</div>
+
+## Overview
+
+Coloss/Serpe-7B-Instruct is a 7 billion parameter language model developed by Coloss, based on the qwen2.5-7B-Instruct architecture. It is specifically fine-tuned for cybersecurity tasks and enhanced with agent capabilities. The model underwent further optimization using DPO with manually curated examples to improve its performance and alignment.
+
+## Key Features
+
+- Based on qwen2.5-7B-Instruct
+- Specialized in cybersecurity tasks, including offensive security
+- Enhanced with agent capabilities
+- Fine-tuned using a curated cybersecurity dataset
+- Optimized with DPO using manually curated examples
+- Aligned and refuses to answer to toxic questions
+
+## Intended Use
+
+Serpe-7B is designed for cybersecurity professionals, researchers, and enthusiasts. It can assist with:
+
+- Vulnerability analysis
+- Threat detection and response
+- Security policy formulation
+- Code review for security issues
+- Incident response planning
+- Offensive security tasks and simulations
+- Penetration testing support
+- Exploit development assistance
+
+## Training Procedure
+
+1. Initial fine-tuning on the curated cybersecurity dataset
+2. Further optimization using DPO with manually curated examples
+
+## Ethical Considerations
+
+- The model's specialization in cybersecurity, including offensive security capabilities, makes it particularly sensitive to misuse. Users must strictly adhere to all relevant laws, ethical guidelines, and have proper authorization before using the model for any offensive security tasks.
+- While the model has undergone DPO to improve alignment, users should still exercise extreme caution and verify outputs, especially for critical security decisions or offensive security operations.
+- The model's knowledge is based on its training data and may not reflect the most current cybersecurity threats, techniques, or best practices.
+- Users must ensure that any offensive security applications of this model are conducted in controlled, authorized environments only.
+
+## Limitations
+
+- It may not be suitable for non-cybersecurity related tasks.
+- As with all language models, it can produce incorrect or biased information.
+- Users should not rely solely on the model for making critical security decisions or conducting offensive security operations without expert human oversight.
+- The model's offensive security capabilities should be used with extreme caution and only by qualified professionals in appropriate, authorized contexts.