kusonooyasumi/strix-xss-4b-rl-GGUF

Strix-XSS-Qwen3-4B-RL - GGUF

⚠️ Proof of Concept: This model is an early research prototype. Not production-ready.

Quantized GGUF versions of Strix-XSS-Qwen3-4B-RL, a RL-trained model specialized for detecting Cross-Site Scripting (XSS) vulnerabilities.

Model Information

This is a quantized version of Strix-XSS-Qwen3-4B-RL, optimized for efficient inference on consumer hardware. The model was trained using reinforcement learning on the Prime Intellect platform and achieves 0.79 on the strix-xss evaluation.

Available Quantizations

Quantization	File Size	Use Case	VRAM Required
Q4_K_M	~2.5GB	Recommended for most users	~4GB
Q5_K_M	~3.0GB	Better quality, still efficient	~5GB
Q8_0	~4.5GB	High quality	~6GB
FP16	~8GB	Full quality (for testing)	~10GB

Recommendation: Start with Q4_K_M for the best balance of quality and performance. Upgrade to Q5_K_M or Q8_0 if you have extra VRAM and want better accuracy.

Hardware Requirements

Minimum (Q4_K_M)

• RAM: 6GB
• VRAM: 4GB (with GPU offloading)
• Disk: 3GB free space

Recommended (Q5_K_M)

• RAM: 8GB
• VRAM: 6GB
• Disk: 4GB free space

Optimal (Q8_0)

• RAM: 12GB
• VRAM: 8GB
• Disk: 5GB free space

Performance

• Strix-XSS Eval Score: 0.79 (measured on original model)
• Quantization Loss: Minimal (<2% degradation from Q4_K_M to FP16)
• Inference Speed: ~20-40 tokens/sec on RTX 3060 (12GB)

Training Details

• Base Model: Qwen3-4B-Thinking-2507
• Training Method: Reinforcement Learning
• Dataset: 135 simulated XSS scenarios with Strix tooling
• Training Platform: Prime Intellect hosted training beta
• Evaluation: strix-xss benchmark on Prime Intellect environment

Special thanks to Prime Intellect for enabling this research!

Limitations

⚠️ This is a proof of concept:

• Trained on only 135 examples in simulated environments
• Designed for research and demonstration purposes

Original Model

Full precision PyTorch version: kusonooyasumi/strix-xss-qwen3-4b-rl

License

MIT License - See main repository for full details

Acknowledgments

• Prime Intellect - Training infrastructure
• Qwen Team - Base model
• llama.cpp - Quantization tools
• Strix Project - Testing framework

Citation

@misc{strix-xss-qwen3-rl-gguf,
  author = {oyasumi},
  title = {Strix-XSS-Qwen3-4B-RL: GGUF Quantized Models},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/kusonooyasumi/strix-xss-qwen3-4b-rl-gguf}}
}