reaperdoesntknow/Qwen3.5-2B-CyberSec

VISIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2.3BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 9, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

reaperdoesntknow/Qwen3.5-2B-CyberSec is a 2.3 billion parameter Qwen3.5-based language model fine-tuned specifically for cybersecurity instruction prompts. Developed by reaperdoesntknow, it leverages the Trendyol Cybersecurity Instruction Tuning Dataset to specialize in cybersecurity-related queries. This model is designed for research and local experimentation in cybersecurity, offering capabilities for understanding and responding to technical security instructions, and supports both text and vision components.

Loading preview...

Qwen3.5-2B-CyberSec Overview

reaperdoesntknow/Qwen3.5-2B-CyberSec is a 2.3 billion parameter model based on the Qwen3.5 architecture, specifically fine-tuned for cybersecurity instruction prompts. It utilizes the Trendyol Cybersecurity Instruction Tuning Dataset to enhance its understanding and generation capabilities in the cybersecurity domain. This model is intended for research and local experimentation, providing a specialized tool for exploring small-model responses to complex security queries.

Key Capabilities

  • Cybersecurity Instruction Response: Optimized to process and generate responses to prompts related to cybersecurity topics.
  • Multimodal Architecture: Inherits Qwen3.5's architecture, supporting both text and vision components, though its primary fine-tuning is text-based for cybersecurity.
  • Research and Prototyping: Ideal for academic research, local prototyping, and qualitative evaluation of cybersecurity-focused LLMs.
  • Comparison Base: Can be used to compare performance against the upstream unsloth/Qwen3.5-2B checkpoint.

Good for

  • Research on Cybersecurity LLMs: Investigating how smaller models handle cybersecurity-specific instructions.
  • Local Experimentation: Developing and testing cybersecurity tools or applications in a controlled environment.
  • Conversion and Quantization: Exploring different model formats and optimizations (e.g., GGUF builds).
  • Educational Purposes: Understanding fundamental cybersecurity concepts through interactive prompts.

Limitations and Safety

It is crucial to note that this model is for research; it can generate incorrect or unsafe technical guidance. It has not undergone formal benchmark or safety evaluations, and should not be used as a validated cybersecurity authority. Users must review and isolate any generated commands and not rely on the model for critical security decisions.

Finetunes
2 models