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license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
tags:
- cybersecurity
- penetration-testing
- exploit-development
- offensive-security
- lora
- qwen
- code
datasets:
- ezradixon/zerosectraining
language:
- en
pipeline_tag: text-generation
---
# ZeroSec-7B 🔴
A QLoRA fine-tune of Qwen2.5-Coder-7B on 29,588 exploit and security samples.
**Built by [The Zero Project](https://github.com/ezradixon) — use only for authorized security testing.**
## What is this?
ZeroSec-7B is a specialist language model trained exclusively on offensive security data:
- **15,023 exploit code files** (Python, C, Perl, Ruby, PHP) from ExploitDB
- **13,468 vulnerability detection rules** from Nuclei templates
- **576 attack payloads** from PayloadsAllTheThings (SQLi, XSS, CMDi, SSTI, SSRF, etc.)
- **523 methodology & reference documents** on exploitation techniques
It's designed to assist with legitimate penetration testing, vulnerability research, and security education.
## Quick Start
### Option 1: Merge + run locally (recommended)
```bash
# 1. Download the LoRA adapter
git lfs install
git clone https://huggingface.co/ezradixon/ZeroSec-7B
# 2. Merge with base model
python merge_and_export.py
# 3. Quantize for local inference (Mac/CPU)
llama-quantize merged-model-f16.gguf Q4_K_M zerosect-7b-Q4_K_M.gguf
# 4. Run
llama-server -m zerosect-7b-Q4_K_M.gguf -ngl 99
```
### Option 2: Python inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base_model = "Qwen/Qwen2.5-Coder-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, "ezradixon/ZeroSec-7B")
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
prompt = "Write a Python script to check for SQL injection in a login form"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Details
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-Coder-7B-Instruct |
| Method | QLoRA (4-bit quantization) |
| GPU | NVIDIA A10G (24GB) |
| Training samples | 29,588 |
| Max sequence length | 1024 tokens |
| Batch size | 2 (effective 8 with grad accum) |
| Epochs | 2 |
| Learning rate | 2e-4 |
| LoRA rank | 16, alpha 32 |
| Training time | ~2 hours |
| Cost | ~$2.00 (Modal serverless) |
## Limitations
- **7B parameters** — not as capable as larger models (GPT-4, Claude)
- **Training data quality varies** — ExploitDB contains legacy code that may not work on modern systems
- **No real-world validation** — exploits were not tested against live targets
- **English only** — training data is overwhelmingly English
## ⚠️ Disclaimer
**This model is for authorized security testing and education only.**
Do not use ZeroSec-7B for:
- Attacking systems without explicit written permission
- Developing malware or ransomware
- Any illegal activity whatsoever
The Zero Project and the model's creator are not responsible for any misuse.
## Author
**Ezra Dixon** — [The Zero Project](https://github.com/ezradixon)
Built on Modal cloud GPUs. 🇦🇺
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