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Model: oxdev/security-auditor-grpo Source: Original Platform
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README.md
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README.md
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base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
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library_name: transformers
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model_name: security-auditor-grpo
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tags:
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- generated_from_trainer
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- grpo
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- trl
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- security
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- smart-contracts
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- solidity
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- audit
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- web3
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license: apache-2.0
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datasets:
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- oxdev/smart-contract-security-sft
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- oxdev/smart-contract-security-audit-v2
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pipeline_tag: text-generation
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language:
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- en
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---
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# 🔐 Smart Contract Security Auditor (GRPO)
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A specialized **smart contract security auditor** built on [Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct), fine-tuned using **Group Relative Policy Optimization (GRPO)** on real-world audit findings from top security firms.
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## 🎯 What It Does
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Given a Solidity smart contract, this model identifies security vulnerabilities and produces structured audit findings with:
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- Vulnerability classification (reentrancy, access control, oracle manipulation, etc.)
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- Severity assessment (Critical/High/Medium/Low)
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- Detailed description of the vulnerability
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- Impact analysis
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- Proof of concept exploit code
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- Recommended fixes
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model = AutoModelForCausalLM.from_pretrained(
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"oxdev/security-auditor-grpo",
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use_cache=True, # Important: config has use_cache=False from training
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)
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tokenizer = AutoTokenizer.from_pretrained("oxdev/security-auditor-grpo")
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device="cuda")
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messages = [
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{"role": "system", "content": "You are an expert smart contract security auditor. Analyze the provided Solidity code for vulnerabilities."},
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{"role": "user", "content": """Audit this contract:
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```solidity
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contract SimpleBank {
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mapping(address => uint256) public balances;
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function deposit() public payable { balances[msg.sender] += msg.value; }
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function withdraw(uint256 amount) public {
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require(balances[msg.sender] >= amount);
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(bool success, ) = msg.sender.call{value: amount}("");
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require(success);
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balances[msg.sender] -= amount;
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}
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}
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```"""},
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]
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result = pipe(messages, max_new_tokens=512, do_sample=False, return_full_text=False)
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output = result[0]["generated_text"]
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if isinstance(output, list):
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output = output[-1]["content"]
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print(output)
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```
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## 🔗 Try It Live
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**Interactive Demo:** [oxdev/security-auditor-demo](https://huggingface.co/spaces/oxdev/security-auditor-demo) — Side-by-side comparison with base model, 7 test cases with known vulnerabilities, automated scoring.
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## Training Details
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### V1 (Current Model)
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- **Method:** GRPO (Group Relative Policy Optimization)
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- **Base Model:** Qwen2.5-Coder-0.5B-Instruct
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- **Dataset:** [oxdev/smart-contract-security-sft](https://huggingface.co/datasets/oxdev/smart-contract-security-sft) (327 synthetic samples)
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- **Hardware:** NVIDIA T4 (16GB)
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- **Epochs:** 2
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- **Reward Functions:** Format compliance, finding rate
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- **Results:**
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- Format reward: 0.025 → 0.40 (**16× improvement**)
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- Finding rate: 0% → 50-75%
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- Mean reward: -0.34 → -0.006
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### V2 (Pending — Colab Notebook Ready)
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- **Dataset:** [oxdev/smart-contract-security-audit-v2](https://huggingface.co/datasets/oxdev/smart-contract-security-audit-v2) (50,902 real audit findings)
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- **Sources:** SkywardNomad92/smart-contract-audit-findings, samscrack/cyfrin-audit-findings, Solodit API
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- **4 Reward Functions:** Format (0.25), Severity matching (0.25), Category matching (0.25), Quality (0.25)
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- **Train on Colab:** Open [`train_grpo_v2_colab.ipynb`](https://huggingface.co/oxdev/security-auditor-grpo/blob/main/train_grpo_v2_colab.ipynb) in Google Colab with a free T4 GPU
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## Vulnerability Categories Covered
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| Category | Keywords |
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|----------|----------|
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| Reentrancy | reentrancy, reentrant, callback |
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| Access Control | unauthorized, permission, onlyowner |
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| Oracle Manipulation | price feed, chainlink, twap |
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| Flash Loan | flash loan, flashloan |
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| Overflow/Underflow | overflow, underflow, arithmetic |
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| Front-running | front-run, sandwich, MEV |
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| DoS | denial of service, gas limit, unbounded |
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| Token Issues | fee-on-transfer, rebasing, ERC20 |
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| Storage | storage collision, delegatecall, proxy |
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| Cross-chain | bridge, relay, message passing |
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| Liquidation | liquidation, collateral, health factor |
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| Signature | ecrecover, replay, nonce, EIP712 |
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| Initialization | uninitialized, constructor |
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| Rounding | precision, truncation, decimal |
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## Architecture
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- **Model:** Qwen2ForCausalLM
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- **Parameters:** 0.5B
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- **Hidden Size:** 896
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- **Layers:** 24
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- **Attention Heads:** 14 (2 KV heads)
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- **Context Length:** 32,768 tokens
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- **Chat Template:** ChatML (`<|im_start|>` / `<|im_end|>`)
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## ⚠️ Important Notes
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1. **Set `use_cache=True`** when loading for inference — the saved config has `use_cache=False` from training, which makes generation 10-20× slower
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2. **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
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3. **V1 was trained on 327 samples** — V2 training on 50K real findings will significantly improve quality
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## Files
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| File | Description |
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|------|-------------|
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| `model.safetensors` | V1 trained model weights (1.8GB) |
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| `train_grpo_job.py` | V1 training script |
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| `train_grpo_v2.py` | V2 training script (4 reward functions) |
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| `train_grpo_v2_colab.ipynb` | V2 Colab notebook (free T4 GPU) |
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| `checkpoint-300/` | V1 training checkpoint |
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| `checkpoint-326/` | V1 final checkpoint |
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## Related Resources
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- **GitHub:** [0xedev/skills](https://github.com/0xedev/skills) — Pashov Audit Group AI-powered security skills
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- **V2 Dataset:** [oxdev/smart-contract-security-audit-v2](https://huggingface.co/datasets/oxdev/smart-contract-security-audit-v2)
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- **Demo Space:** [oxdev/security-auditor-demo](https://huggingface.co/spaces/oxdev/security-auditor-demo)
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## Framework Versions
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- TRL: 1.2.0
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- Transformers: 5.6.2
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- PyTorch: 2.6.0+cu126
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- Datasets: 4.8.4
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## Citations
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```bibtex
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@article{shao2024deepseekmath,
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title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
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author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and others},
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year = 2024,
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eprint = {arXiv:2402.03300},
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}
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```
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