README.md

---
library_name: peft
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
tags:
  - peft
  - lora
  - sequence-classification
  - solidity
  - smart-contract
  - vulnerability-detection
  - access-control
pipeline_tag: text-classification
license: apache-2.0
---

# Solidity Vulnerability Classifier — Access Control

Binary classifier that detects **Access Control** vulnerabilities in Solidity smart contracts.

## Model Details

- **Base model**: [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
- **Method**: QLoRA (4-bit NF4) + classification head
- **Task**: Sequence Classification (2 labels: safe / vulnerable)
- **LoRA rank**: 16, targeting q_proj, k_proj, v_proj, o_proj
- **Classification head**: `modules_to_save=["score"]`

## Available Checkpoints

Load a specific checkpoint with `revision=`:
```python
model = PeftModel.from_pretrained(base, "jhsu12/solidity-vuln-cls-access-control-v1", revision="checkpoint-200")
```

| Tag | Step |
|-----|------|
| `checkpoint-28` | 28 |
| `checkpoint-56` | 56 |
| `checkpoint-84` | 84 |
| `checkpoint-112` | 112 |
| `checkpoint-140` | 140 ← `main` |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, BitsAndBytesConfig
from peft import PeftModel
import torch

base_model = "Qwen/Qwen2.5-Coder-3B-Instruct"
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)

model = AutoModelForSequenceClassification.from_pretrained(
    base_model, num_labels=2, quantization_config=bnb_config,
    device_map="auto", trust_remote_code=True, ignore_mismatched_sizes=True)
model = PeftModel.from_pretrained(model, "jhsu12/solidity-vuln-cls-access-control-v1")
model.eval()

tokenizer = AutoTokenizer.from_pretrained("jhsu12/solidity-vuln-cls-access-control-v1", trust_remote_code=True)

code = "pragma solidity ^0.8.0; contract Example { ... }"
inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=1536).to(model.device)
with torch.no_grad():
    logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1)
print(f"Safe: {probs[0][0]:.2%}, Vulnerable: {probs[0][1]:.2%}")
```

Or use the inference script:
```bash
python inference_classifier.py --checkpoint jhsu12/solidity-vuln-cls-access-control-v1 --file contract.sol
```

## Part of

This is one of 5 expert classifiers in the
[Solidity Vulnerability Detector](https://huggingface.co/jhsu12/solidity-vulnerability-detector) system.

| Expert | Hub Repo |
|--------|----------|
| Reentrancy | `jhsu12/solidity-vuln-cls-reentrancy-v1` |
| Access Control | `jhsu12/solidity-vuln-cls-access-control-v1` |
| Integer Overflow/Underflow | `jhsu12/solidity-vuln-cls-integer-overflow-underflow-v1` |
| Timestamp Dependence | `jhsu12/solidity-vuln-cls-timestamp-dependence-v1` |
| Unchecked Low-Level Calls | `jhsu12/solidity-vuln-cls-unchecked-low-level-calls-v1` |