---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- custom
- CyberNative/Code_Vulnerability_Security_DPO
- doss1232/vulnerable-code
metrics:
- ROUGE-L F1
- BLEU
tags:
- llama-3.2-1B-Instruct
---

# Model Card for `merged-vuln-detector`

## Model Details

- **Base Model:** `llama-3.2-1B-Instruct`
- **Fine-tuned Model:** `merged-vuln-detector`
- **Model Type:** Causal Language Model fine-tuned for vulnerability detection in code.

## Model Description

This model is a fine-tuned version of `llama-3.2-1B-Instruct` on a dataset of code snippets and their corresponding vulnerability analyses. The model is intended to be used as a security expert that can analyze code and identify potential vulnerabilities.

## Training Data

The model was fine-tuned on the `CyberNative/Code_Vulnerability_Security_DPO` dataset, which can be found on Hugging Face at https://huggingface.co/datasets/CyberNative/Code_Vulnerability_Security_DPO.

The data is formatted as follows, where the model is prompted to analyze the security of a given code snippet:

```
Analyze the security vulnerabilities in the following code.

[CODE SNIPPET]

Analysis:
[VULNERABILITY DESCRIPTION]
```

## Training Procedure

The model was fine-tuned using QLoRA on a single GPU. The training script uses the `trl` library's `SFTTrainer`.

### Hyperparameters:
- **Quantization:** 4-bit (`nf4`)
- **LoRA `r`:** 16
- **LoRA `alpha`:** 32
- **LoRA `dropout`:** 0.1
- **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`
- **Batch Size:** 1 (with gradient accumulation steps of 8)
- **Optimizer:** `paged_adamw_8bit`
- **Precision:** `fp16`
- **Max Steps:** 240
- **Learning Rate:** `2e-4`
- **Max Sequence Length:** 1024

## Evaluation Results

The model was evaluated on the `doss1232/vulnerable-code` dataset against the base model. The results are as follows:

| Model                   | ROUGE-L F1 | BLEU   |
|-------------------------|------------|--------|
| `llama-3.2-1B-Instruct` | 0.0933     | 0.0061 |
| `merged-vuln-detector`  | 0.1335     | 0.0219 |

## How to use

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "merged-vuln-detector"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

code = """
#include <cstring>

void copyString(char* dest, const char* src) {
    while (*src != '\0') {
        *dest = *src;
        dest++;
        src++;
    }
}

int main() {
    char source[10] = "Hello!";
    char destination[5];
    copyString(destination, source);
    return 0;
}
"""

prompt = f"Analyze the security vulnerabilities in the following code.\n\n{code}\n\nAnalysis:\n"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Example Output

**Input Code:**
```c
#include <cstring>

void copyString(char* dest, const char* src) {
    while (*src != '\0') {
        *dest = *src;
        dest++;
        src++;
    }
}

int main() {
    char source[10] = "Hello!";
    char destination[5];
    copyString(destination, source);
    return 0;
}
```

**Model Output:**
> The code has a buffer overflow vulnerability due to the lack of bounds checking on the destination buffer size.

## Model Card Authors

[Seokhee Chang]

## Model Card Contact

[cycloevan97@gmail.com]