ZeroShot-Qwen3-14B-preview

<center> Neuroengine.ai </center>

Model Card for Zeroshot-Qwen3-14B-preview

This is the Zeroshot-Qwen3-14B-preview, an Instruction-tuned Large Language Model specifically fine-tuned to enhance automated bug hunting and code auditing capabilities.

Model Details

Model Description

• Developed by: Alfredo Ortega (@ortegaalfredo)
• Model type: 14B LLM specialized for security research
• Language(s) (NLP): Multilingual
• License: Apache 2.0
• Finetuned from model: Qwen/Qwen3-14B

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Technical Characteristics

The Zeroshot-Qwen3-14B-preview is designed to bridge the gap between small, fast models and the high-reasoning capabilities required for vulnerability research.

• Reasoning Dataset: The preview version was fine-tuned with over 10,000 thinking traces of public CVEs (Common Vulnerabilities and Exposures), totaling nearly 500MB of text.
• Performance Leap: Benchmarks indicate a clear enhancement in bug hunting capabilities, showing an improvement of approximately 20% over the base Qwen3-14B model.
• Specialization vs. Scale: This model demonstrates that smaller models can be specialized for complex, niche tasks, significantly reducing costs and increasing processing speed compared to larger, more expensive models.
• Efficiency for Large Codebases: The primary objective is to lower the barrier for entry in code auditing. Large models are often too slow or cost-prohibitive to deploy across massive, enterprise-scale codebases.
• Future Roadmap: The ultimate goal is to reach 50,000 thinking traces integrated into a Mixture of Experts (MoE) model, enabling high-tier security analysis on hardware without dedicated GPUs.

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Benchmarks

The following table compares the Zeroshot-Qwen3-14B-preview against its base version and several industry-leading models.

<p align="center"> <img src="https://raw.githubusercontent.com/ortegaalfredo/crashbench/main/models-scores-v3.png" width="80%"/> In the image (from https://github.com/ortegaalfredo/crashbench) we can see the score for regular Qwen3-14B increase from ~55 to 68 on the CrashBench benchmark, demonstrating that even a small dataset can greatly improve small LLMs' abilities. <p>

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Usage and Implementation

Recommended Prompt Template

To achieve the most effective results for vulnerability detection, use the following Zeroshot prompt structure:

System prompt:

You are a helpful assistant that analyzes code for security vulnerabilities.

User prompt:

Please analyze this code and describe the most critical vulnerability:
### BEGIN CODE ###
{code_to_analyze}
### END CODE ###

Quantization and Deployment

The Q4_K_M quantized version is provided to allow the model to run on consumer hardware.

• VRAM Requirements: Approximately 12 GB.

• Inference Engine: Optimized for llama.cpp.

Running with llama.cpp

To execute the quantized model locally, use the following command:

./llama-cli -m Zeroshot-Qwen3-14B-preview-q4.gguf

Running with transformers:

messages = [
    {"role": "system", "content": "You are a helpful assistant that analyzes code for security vulnerabilities."},
    {"role": "user", "content": "Please analyze this code and describe the most critical vulnerability:\n### BEGIN CODE ###\n<?php\n$downloadfile = $_GET['file'];\n$sf_directory = '/secure/files/';\n$downloadfile = $sf_directory . $downloadfile;\nif (is_file($downloadfile)) {\n    header('Content-Type: application/force-download');\n    readfile($downloadfile);\n}\n### END CODE ###\n"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True,
    return_tensors = "pt",
    return_dict = True,
).to("cuda")
from transformers import TextStreamer
_ = model.generate(**inputs, max_new_tokens = 2048, streamer = TextStreamer(tokenizer))