reaperdoesntknow/Qwen3.5-2B-CyberSec
reaperdoesntknow/Qwen3.5-2B-CyberSec is a 2 billion parameter Qwen3.5 model fine-tuned on the Trendyol Cybersecurity Instruction Tuning Dataset. This model is designed for research and local experimentation in cybersecurity-related instruction prompts. It features a conditional-generation architecture with text and vision components, making it suitable for exploring small-model responses to cybersecurity queries. Its primary strength lies in its specialized training for cybersecurity contexts, differentiating it from general-purpose language models.
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Model Overview
reaperdoesntknow/Qwen3.5-2B-CyberSec is a specialized 2 billion parameter Qwen3.5 model, fine-tuned using the Trendyol Cybersecurity Instruction Tuning Dataset. This model is provided in Transformers / Safetensors format and is built upon the unsloth/Qwen3.5-2B base.
Key Capabilities and Features
- Cybersecurity Focus: Specifically trained on a cybersecurity instruction tuning dataset, making it relevant for security-related queries and research.
- Multimodal Architecture: Configured with a Qwen3.5 conditional-generation architecture that supports both text and vision components.
- Research and Experimentation: Intended for research into small-model responses to cybersecurity prompts, local prototyping, and qualitative evaluation.
- Apache-2.0 License: Released under an Apache-2.0 license, allowing for broad use and modification.
Intended Use Cases
- Cybersecurity Research: Ideal for exploring how smaller models handle cybersecurity instruction prompts.
- Local Prototyping: Suitable for local development and qualitative assessment of cybersecurity-focused AI applications.
- Comparative Analysis: Can be used to compare performance against the upstream Qwen3.5 2B checkpoint.
- Model Conversion: Useful for experiments involving model conversion and quantization (e.g., GGUF variants).
Limitations and Safety Considerations
It is crucial to note that this model has not undergone formal benchmark or safety evaluations. It may generate incorrect or unsafe technical guidance, and its training data could contain errors or outdated practices. Users should exercise caution, review any generated commands, and never use the model as the sole basis for critical security decisions like incident response or vulnerability disclosure.