---
library_name: transformers
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
tags:
  - smart-contracts
  - solidity
  - security
  - vulnerability-detection
  - grpo
  - lora
  - reinforcement-learning
language:
  - en
license: mit
---

# DeepSeek-R1-Distill-Qwen-7B — Smart Contract Vulnerability Detection

RL fine-tuned version of DeepSeek-R1-Distill-Qwen-7B for detecting vulnerabilities in Solidity smart contracts. Fine-tuned using GRPO (Group Relative Policy Optimization) with LoRA on the CGT (Consolidated Ground Truth) dataset.

---

## Model Description

- **Base model:** deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
- **Fine-tuning method:** GRPO + LoRA
- **Task:** Smart contract vulnerability detection and classification
- **Developer:** Nishant Pandav ([@npanium](https://github.com/npanium))
- **Repository:** [https://github.com/npanium/smartcontracts-vulnerability-r1](https://github.com/npanium/smartcontracts-vulnerability-r1)
- **License:** MIT

---

## What it does

Given a Solidity smart contract, the model:
1. Reasons through the code using chain-of-thought inside `<think>` tags
2. Determines whether the contract is vulnerable
3. Classifies the vulnerability by DASP category (1–9) and SWC ID (100–136)

Output format:
```
<think>
... reasoning about the contract ...
</think>
VULNERABLE: yes/no
DASP_CATEGORY: N
SWC_ID: NNN
EXPLANATION: ...
```

---

## Results

Evaluated on 1,478 held-out contracts from the CGT dataset:

| Metric | Before (base) | After (fine-tuned) | Delta |
|--------|--------------|-------------------|-------|
| Detection accuracy (Tier 1) | 23.0% | 74.3% | **+51.3%** |
| DASP category (Tier 2) | 8.8% | 11.3% | +2.5% |
| SWC ID (Tier 3) | 3.0% | 0.0% | -3.0% |
| Overall | 11.6% | 28.5% | **+16.9%** |
| Parse failure rate | 40.2% | 0.0% | **-40.2%** |

---

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "npanium/deepseek-r1-qwen7b-smartcontract-grpo"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

contract = """
pragma solidity ^0.4.18;
contract Vulnerable {
    mapping(address => uint) public balances;

    function withdraw(uint _amount) public {
        if (balances[msg.sender] >= _amount) {
            msg.sender.call.value(_amount)();
            balances[msg.sender] -= _amount;
        }
    }
}
"""

prompt = f"""Analyze this Solidity smart contract for security vulnerabilities.
Think step by step inside <think> tags, then provide your assessment.

``solidity
{contract}``


Use this exact format:
VULNERABLE: yes/no
DASP_CATEGORY: [1-9]
SWC_ID: [100-136]
EXPLANATION: [one sentence]"""

messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True,
)
if hasattr(inputs, "input_ids"):
    inputs = inputs.input_ids
inputs = inputs.to(model.device)

with torch.no_grad():
    outputs = model.generate(
        inputs,
        max_new_tokens=1024,
        temperature=0.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(
    outputs[0][inputs.shape[1]:],
    skip_special_tokens=True,
)
print(response)
```

---

## Training Details

### Dataset

**CGT (Consolidated Ground Truth)** — [github.com/gsalzer/cgt](https://github.com/gsalzer/cgt)

Consolidates 13 prior smart contract vulnerability datasets. Labels cross-validated across source datasets.

| Split | Examples |
|-------|----------|
| Train | 5,910 |
| Test (locked) | 1,478 |

### Training Hyperparameters

| Parameter | Value |
|-----------|-------|
| Training regime | bf16 mixed precision |
| Learning rate | 5e-6 |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA target modules | q_proj, v_proj, k_proj, o_proj |
| Generations per prompt | 8 |
| Max completion length | 1024 |
| Gradient accumulation steps | 8 |
| Epochs | 1 |

### Hardware

- **GPU:** NVIDIA A100 SXM4 80GB
- **Cloud provider:** Fluence Network
- **Training duration:** ~48 hours

---

## Limitations

**Outcome reward only.** The reward function validates whether the final label is correct, not whether the reasoning is valid. The model may produce plausible-sounding analysis that doesn't actually justify the conclusion.

**SWC ID regression.** Post-training SWC ID accuracy dropped to zero. The model prioritised the higher-weighted binary detection reward at the expense of fine-grained weakness classification.

**Context window.** Contracts exceeding ~4,000 characters were excluded from training. Performance on very large contracts is untested.

---

## Citation

```bibtex
@misc{pandav2026scvulnrl,
  author = {Pandav, Nishant},
  title  = {Smart Contract Vulnerability Detection via RL Fine-Tuning},
  year   = {2026},
  url    = {https://github.com/npanium/smartcontracts-vulnerability-r1}
}
```