Instructions to use oxdev/security-auditor-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oxdev/security-auditor-grpo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="oxdev/security-auditor-grpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oxdev/security-auditor-grpo")
model = AutoModelForCausalLM.from_pretrained("oxdev/security-auditor-grpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use oxdev/security-auditor-grpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oxdev/security-auditor-grpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oxdev/security-auditor-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/oxdev/security-auditor-grpo

SGLang

How to use oxdev/security-auditor-grpo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "oxdev/security-auditor-grpo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oxdev/security-auditor-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "oxdev/security-auditor-grpo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oxdev/security-auditor-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use oxdev/security-auditor-grpo with Docker Model Runner:
```
docker model run hf.co/oxdev/security-auditor-grpo
```

🔐 Smart Contract Security Auditor (GRPO)

A specialized smart contract security auditor built on Qwen2.5-Coder-0.5B-Instruct, fine-tuned using Group Relative Policy Optimization (GRPO) on real-world audit findings from top security firms.

🎯 What It Does

Given a Solidity smart contract, this model identifies security vulnerabilities and produces structured audit findings with:

Vulnerability classification (reentrancy, access control, oracle manipulation, etc.)
Severity assessment (Critical/High/Medium/Low)
Detailed description of the vulnerability
Impact analysis
Proof of concept exploit code
Recommended fixes

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model = AutoModelForCausalLM.from_pretrained(
    "oxdev/security-auditor-grpo",
    use_cache=True,  # Important: config has use_cache=False from training
)
tokenizer = AutoTokenizer.from_pretrained("oxdev/security-auditor-grpo")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device="cuda")

messages = [
    {"role": "system", "content": "You are an expert smart contract security auditor. Analyze the provided Solidity code for vulnerabilities."},
    {"role": "user", "content": """Audit this contract:
```solidity
contract SimpleBank {
    mapping(address => uint256) public balances;
    function deposit() public payable { balances[msg.sender] += msg.value; }
    function withdraw(uint256 amount) public {
        require(balances[msg.sender] >= amount);
        (bool success, ) = msg.sender.call{value: amount}("");
        require(success);
        balances[msg.sender] -= amount;
    }
}
```"""},
]

result = pipe(messages, max_new_tokens=512, do_sample=False, return_full_text=False)
output = result[0]["generated_text"]
if isinstance(output, list):
    output = output[-1]["content"]
print(output)

🔗 Try It Live

Interactive Demo: oxdev/security-auditor-demo — Side-by-side comparison with base model, 7 test cases with known vulnerabilities, automated scoring.

Training Details

V1 (Current Model)

Method: GRPO (Group Relative Policy Optimization)
Base Model: Qwen2.5-Coder-0.5B-Instruct
Dataset: oxdev/smart-contract-security-sft (327 synthetic samples)
Hardware: NVIDIA T4 (16GB)
Epochs: 2
Reward Functions: Format compliance, finding rate
Results:
- Format reward: 0.025 → 0.40 (16× improvement)
- Finding rate: 0% → 50-75%
- Mean reward: -0.34 → -0.006

V2 (Pending — Colab Notebook Ready)

Dataset: oxdev/smart-contract-security-audit-v2 (50,902 real audit findings)
Sources: SkywardNomad92/smart-contract-audit-findings, samscrack/cyfrin-audit-findings, Solodit API
4 Reward Functions: Format (0.25), Severity matching (0.25), Category matching (0.25), Quality (0.25)
Train on Colab: Open train_grpo_v2_colab.ipynb in Google Colab with a free T4 GPU

Vulnerability Categories Covered

Category	Keywords
Reentrancy	reentrancy, reentrant, callback
Access Control	unauthorized, permission, onlyowner
Oracle Manipulation	price feed, chainlink, twap
Flash Loan	flash loan, flashloan
Overflow/Underflow	overflow, underflow, arithmetic
Front-running	front-run, sandwich, MEV
DoS	denial of service, gas limit, unbounded
Token Issues	fee-on-transfer, rebasing, ERC20
Storage	storage collision, delegatecall, proxy
Cross-chain	bridge, relay, message passing
Liquidation	liquidation, collateral, health factor
Signature	ecrecover, replay, nonce, EIP712
Initialization	uninitialized, constructor
Rounding	precision, truncation, decimal

Architecture

Model: Qwen2ForCausalLM
Parameters: 0.5B
Hidden Size: 896
Layers: 24
Attention Heads: 14 (2 KV heads)
Context Length: 32,768 tokens
Chat Template: ChatML (<|im_start|> / <|im_end|>)

⚠️ Important Notes

Set use_cache=True when loading for inference — the saved config has use_cache=False from training, which makes generation 10-20× slower
This is a 0.5B model — it's fast but not as capable as larger models. Use it for quick triage, not as a replacement for professional audits
V1 was trained on 327 samples — V2 training on 50K real findings will significantly improve quality

Files

File	Description
`model.safetensors`	V1 trained model weights (1.8GB)
`train_grpo_job.py`	V1 training script
`train_grpo_v2.py`	V2 training script (4 reward functions)
`train_grpo_v2_colab.ipynb`	V2 Colab notebook (free T4 GPU)
`checkpoint-300/`	V1 training checkpoint
`checkpoint-326/`	V1 final checkpoint

Related Resources

GitHub: 0xedev/skills — Pashov Audit Group AI-powered security skills
V2 Dataset: oxdev/smart-contract-security-audit-v2
Demo Space: oxdev/security-auditor-demo

Framework Versions

TRL: 1.2.0
Transformers: 5.6.2
PyTorch: 2.6.0+cu126
Datasets: 4.8.4

Citations

@article{shao2024deepseekmath,
    title   = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author  = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and others},
    year    = 2024,
    eprint  = {arXiv:2402.03300},
}