---
license: apache-2.0
base_model: bigcode/starcoder2-15b-instruct-v0.1
tags:
- code
- security
- starcoder
- bigcode
- securecode
- owasp
- vulnerability-detection
datasets:
- scthornton/securecode-v2
language:
- en
library_name: transformers
pipeline_tag: text-generation
arxiv: 2512.18542
---

# StarCoder2 15B - SecureCode Edition

<div align="center">

[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Training Dataset](https://img.shields.io/badge/dataset-SecureCode%20v2.0-green.svg)](https://huggingface.co/datasets/scthornton/securecode-v2)
[![Base Model](https://img.shields.io/badge/base-StarCoder2%2015B-orange.svg)](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)
[![perfecXion.ai](https://img.shields.io/badge/by-perfecXion.ai-purple.svg)](https://perfecxion.ai)

**The most powerful multi-language security model - 600+ programming languages**

[📄 Paper](https://arxiv.org/abs/2512.18542) | [🤗 Model Card](https://huggingface.co/scthornton/starcoder2-15b-securecode) | [📊 Dataset](https://huggingface.co/datasets/scthornton/securecode-v2) | [💻 perfecXion.ai](https://perfecxion.ai)

</div>

---

## 🎯 What is This?

This is **StarCoder2 15B Instruct** fine-tuned on the **SecureCode v2.0 dataset** - the most comprehensive multi-language code model available, trained on **4 trillion tokens** across **600+ programming languages**, now enhanced with production-grade security knowledge.

StarCoder2 represents the cutting edge of open-source code generation, developed by BigCode (ServiceNow + Hugging Face). Combined with SecureCode training, this model delivers:

✅ **Unprecedented language coverage** - Security awareness across 600+ languages
✅ **State-of-the-art code generation** - Best open-source model performance
✅ **Complex security reasoning** - 15B parameters for sophisticated vulnerability analysis
✅ **Production-ready quality** - Trained on The Stack v2 with rigorous data curation

**The Result:** The most powerful and versatile security-aware code model in the SecureCode collection.

**Why StarCoder2 15B?** This model offers:
- 🌍 **600+ languages** - From mainstream to niche (Solidity, Kotlin, Swift, Haskell, etc.)
- 🏆 **SOTA performance** - Best open-source code model
- 🧠 **Complex reasoning** - 15B parameters for sophisticated security analysis
- 🔬 **Research-grade** - Built on The Stack v2 with extensive curation
- 🌟 **Community-driven** - BigCode initiative backed by ServiceNow + HuggingFace

---

## 🚨 The Problem This Solves

**AI coding assistants produce vulnerable code in 45% of security-relevant scenarios** (Veracode 2025). For organizations using diverse tech stacks, this problem multiplies across dozens of languages and frameworks.

**Multi-language security challenges:**
- Solidity smart contracts: **$3+ billion** stolen in Web3 exploits (2021-2024)
- Mobile apps (Kotlin/Swift): Frequent authentication bypass vulnerabilities
- Legacy systems (COBOL/Fortran): Undocumented security flaws
- Emerging languages (Rust/Zig): New security patterns needed

StarCoder2 SecureCode Edition addresses security across the entire programming language spectrum.

---

## 💡 Key Features

### 🌍 Unmatched Language Coverage

StarCoder2 15B trained on **600+ programming languages**:
- **Mainstream:** Python, JavaScript, Java, C++, Go, Rust
- **Web3:** Solidity, Vyper, Cairo, Move
- **Mobile:** Kotlin, Swift, Dart
- **Systems:** C, Rust, Zig, Assembly
- **Functional:** Haskell, OCaml, Scala, Elixir
- **Legacy:** COBOL, Fortran, Pascal
- **And 580+ more...**

Now enhanced with **1,209 security-focused examples** covering OWASP Top 10:2025.

### 🏆 State-of-the-Art Performance

StarCoder2 15B delivers cutting-edge results:
- HumanEval: **72.6%** pass@1 (best open-source at release)
- MultiPL-E: **52.3%** average across languages
- Leading performance on long-context code tasks
- Trained on The Stack v2 (4T tokens)

### 🔐 Comprehensive Security Training

Trained on real-world security incidents:
- **224 examples** of Broken Access Control
- **199 examples** of Authentication Failures
- **125 examples** of Injection attacks
- **115 examples** of Cryptographic Failures
- Complete **OWASP Top 10:2025** coverage

### 📋 Advanced Security Analysis

Every response includes:
1. **Multi-language vulnerability patterns**
2. **Secure implementations** with language-specific best practices
3. **Attack demonstrations** with realistic exploits
4. **Cross-language security guidance** - patterns that apply across languages

---

## 📊 Training Details

| Parameter | Value |
|-----------|-------|
| **Base Model** | bigcode/starcoder2-15b-instruct-v0.1 |
| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) |
| **Training Dataset** | [SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2) |
| **Dataset Size** | 841 training examples |
| **Training Epochs** | 3 |
| **LoRA Rank (r)** | 16 |
| **LoRA Alpha** | 32 |
| **Learning Rate** | 2e-4 |
| **Quantization** | 4-bit (bitsandbytes) |
| **Trainable Parameters** | ~78M (0.52% of 15B total) |
| **Total Parameters** | 15B |
| **Context Window** | 16K tokens |
| **GPU Used** | NVIDIA A100 40GB |
| **Training Time** | ~125 minutes (estimated) |

### Training Methodology

**LoRA fine-tuning** preserves StarCoder2's exceptional multi-language capabilities:
- Trains only 0.52% of parameters
- Maintains SOTA code generation quality
- Adds cross-language security understanding
- Efficient deployment for 15B model

**4-bit quantization** enables deployment on 24GB+ GPUs while maintaining quality.

---

## 🚀 Usage

### Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = "bigcode/starcoder2-15b-instruct-v0.1"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)

# Load SecureCode adapter
model = PeftModel.from_pretrained(model, "scthornton/starcoder2-15b-securecode")

# Generate secure Solidity smart contract
prompt = """### User:
Write a secure ERC-20 token contract with protection against reentrancy, integer overflow, and access control vulnerabilities.

### Assistant:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Multi-Language Security Analysis

```python
# Analyze Rust code for memory safety issues
rust_prompt = """### User:
Review this Rust web server code for security vulnerabilities:

```rust
use actix_web::{web, App, HttpResponse, HttpServer};

async fn user_profile(user_id: web::Path<String>) -> HttpResponse {
    let query = format!("SELECT * FROM users WHERE id = '{}'", user_id);
    let result = execute_query(&query).await;
    HttpResponse::Ok().json(result)
}
```

### Assistant:
"""

# Analyze Kotlin Android code
kotlin_prompt = """### User:
Identify authentication vulnerabilities in this Kotlin Android app:

```kotlin
class LoginActivity : AppCompatActivity() {
    fun login(username: String, password: String) {
        val prefs = getSharedPreferences("auth", MODE_PRIVATE)
        prefs.edit().putString("token", generateToken(username, password)).apply()
    }
}
```

### Assistant:
"""
```

### Production Deployment (4-bit Quantization)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

# 4-bit quantization - runs on 24GB+ GPU
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

model = AutoModelForCausalLM.from_pretrained(
    "bigcode/starcoder2-15b-instruct-v0.1",
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

model = PeftModel.from_pretrained(model, "scthornton/starcoder2-15b-securecode")
tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder2-15b-instruct-v0.1", trust_remote_code=True)
```

---

## 🎯 Use Cases

### 1. **Web3/Blockchain Security**
Analyze smart contracts across multiple chains:
```
Audit this Solidity DeFi protocol for reentrancy, flash loan attacks, and access control issues
```

### 2. **Multi-Language Codebase Security**
Review polyglot applications:
```
Analyze this microservices app (Go backend, TypeScript frontend, Rust services) for security vulnerabilities
```

### 3. **Mobile App Security**
Secure iOS and Android apps:
```
Review this Swift iOS app for authentication bypass and data exposure vulnerabilities
```

### 4. **Legacy System Modernization**
Secure legacy code:
```
Identify security flaws in this COBOL mainframe application and provide modernization guidance
```

### 5. **Emerging Language Security**
Security for new languages:
```
Write a secure Zig HTTP server with memory safety and input validation
```

---

## ⚠️ Limitations

### What This Model Does Well
✅ Multi-language security analysis (600+ languages)
✅ State-of-the-art code generation
✅ Complex security reasoning
✅ Cross-language pattern recognition

### What This Model Doesn't Do
❌ Not a smart contract auditing firm
❌ Cannot guarantee bug-free code
❌ Not legal/compliance advice
❌ Not a replacement for security experts

### Resource Requirements
- **Larger model** - Requires 24GB+ GPU for optimal performance
- **Higher memory** - 40GB+ RAM recommended
- **Longer inference** - Slower than smaller models

---

## 📈 Performance Benchmarks

### Hardware Requirements

**Minimum:**
- 40GB RAM
- 24GB GPU VRAM (with 4-bit quantization)

**Recommended:**
- 64GB RAM
- 40GB+ GPU (A100, RTX 6000 Ada)

**Inference Speed (on A100 40GB):**
- ~60 tokens/second (4-bit quantization)
- ~85 tokens/second (bfloat16)

### Code Generation (Base Model Scores)

| Benchmark | Score | Rank |
|-----------|-------|------|
| HumanEval | 72.6% | Best open-source |
| MultiPL-E | 52.3% | Top 3 overall |
| Long context | SOTA | #1 |

---

## 🔬 Dataset Information

Trained on **[SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2)**:
- **1,209 examples** with real CVE grounding
- **100% incident validation**
- **OWASP Top 10:2025** complete coverage
- **Multi-language security patterns**

---

## 📄 License

**Model:** Apache 2.0 | **Dataset:** CC BY-NC-SA 4.0

Powered by the **BigCode OpenRAIL-M** license commitment.

---

## 📚 Citation

```bibtex
@misc{thornton2025securecode-starcoder2,
  title={StarCoder2 15B - SecureCode Edition},
  author={Thornton, Scott},
  year={2025},
  publisher={perfecXion.ai},
  url={https://huggingface.co/scthornton/starcoder2-15b-securecode}
}
```

---

## 🙏 Acknowledgments

- **BigCode Project** (ServiceNow + Hugging Face) for StarCoder2
- **The Stack v2** contributors for dataset curation
- **OWASP Foundation** for vulnerability taxonomy
- **Web3 security community** for blockchain vulnerability research

---

## 🔗 Related Models

- **[llama-3.2-3b-securecode](https://huggingface.co/scthornton/llama-3.2-3b-securecode)** - Most accessible (3B)
- **[qwen-coder-7b-securecode](https://huggingface.co/scthornton/qwen-coder-7b-securecode)** - Best code model (7B)
- **[deepseek-coder-6.7b-securecode](https://huggingface.co/scthornton/deepseek-coder-6.7b-securecode)** - Security-optimized (6.7B)
- **[codellama-13b-securecode](https://huggingface.co/scthornton/codellama-13b-securecode)** - Enterprise trusted (13B)

[View Collection](https://huggingface.co/collections/scthornton/securecode)

---

<div align="center">

**Built with ❤️ for secure multi-language software development**

[perfecXion.ai](https://perfecxion.ai) | [Contact](mailto:scott@perfecxion.ai)

</div>