README.md

---
license: apache-2.0
base_model: ibm-granite/granite-20b-code-instruct-8k
tags:
  - security
  - cybersecurity
  - secure-coding
  - ai-security
  - owasp
  - code-generation
  - lora
  - fine-tuned
  - securecode
datasets:
  - scthornton/securecode
library_name: peft
pipeline_tag: text-generation
language:
  - code
  - en
---

# Granite 20B Code SecureCode

<div align="center">

![Parameters](https://img.shields.io/badge/params-20B-blue.svg)
![Dataset](https://img.shields.io/badge/dataset-2,372_examples-green.svg)
![OWASP](https://img.shields.io/badge/OWASP-Top_10_2021_+_LLM_Top_10_2025-orange.svg)
![Method](https://img.shields.io/badge/method-bf16_LoRA-purple.svg)

**Security-specialized code model fine-tuned on the [SecureCode](https://huggingface.co/datasets/scthornton/securecode) dataset**

[Dataset](https://huggingface.co/datasets/scthornton/securecode) | [Paper (arXiv:2512.18542)](https://arxiv.org/abs/2512.18542) | [Model Collection](https://huggingface.co/collections/scthornton/securecode) | [perfecXion.ai](https://perfecxion.ai)

</div>

---

## What This Model Does

This model generates **secure code** when developers ask about building features. Instead of producing vulnerable implementations (like 45% of AI-generated code does), it:

- Identifies the security risks in common coding patterns
- Provides vulnerable *and* secure implementations side by side
- Explains how attackers would exploit the vulnerability
- Includes defense-in-depth guidance: logging, monitoring, SIEM integration, infrastructure hardening

The model was fine-tuned on **2,372 security training examples** covering both traditional web security (OWASP Top 10 2021) and AI/ML security (OWASP LLM Top 10 2025).

## Model Details

| | |
|---|---|
| **Base Model** | [Granite 20B Code Instruct 8K](https://huggingface.co/ibm-granite/granite-20b-code-instruct-8k) |
| **Parameters** | 20B |
| **Architecture** | GPT-BigCode |
| **Tier** | Tier 4: XL Model |
| **Method** | bf16 LoRA (no quantization) |
| **LoRA Rank** | 8 (alpha=16) |
| **Target Modules** | `attn.c_attn, attn.c_proj, mlp.c_fc, mlp.c_proj` (4 modules) |
| **Training Data** | [scthornton/securecode](https://huggingface.co/datasets/scthornton/securecode) (2,372 examples) |
| **Hardware** | NVIDIA DGX Spark GB10 (Blackwell, unified memory) |

Largest model in the collection. IBM's enterprise-grade code model with 8K context. Deepest security reasoning capabilities.

## Quick Start

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

# Optional: 4-bit quantization for low-VRAM inference (training was bf16 LoRA)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

base_model = AutoModelForCausalLM.from_pretrained(
    "ibm-granite/granite-20b-code-instruct-8k",
    quantization_config=bnb_config,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("scthornton/granite-20b-code-securecode")
model = PeftModel.from_pretrained(base_model, "scthornton/granite-20b-code-securecode")

# Ask a security-relevant coding question
messages = [
    {"role": "user", "content": "How do I implement JWT authentication with refresh tokens in Python?"}
]

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

## Training Details

### Dataset

Trained on the full **[SecureCode](https://huggingface.co/datasets/scthornton/securecode)** unified dataset:

- **2,372 total examples** (1,625 web security + 747 AI/ML security)
- **20 vulnerability categories** across OWASP Top 10 2021 and OWASP LLM Top 10 2025
- **12+ programming languages** and **49+ frameworks**
- **4-turn conversational structure**: feature request, vulnerable/secure implementations, advanced probing, operational guidance
- **100% incident grounding**: every example tied to real CVEs, vendor advisories, or published attack research

### Hyperparameters

| Parameter | Value |
|-----------|-------|
| LoRA rank | 8 |
| LoRA alpha | 16 |
| LoRA dropout | 0.05 |
| Target modules | 4 linear layers |
| Quantization | None (bf16 base weights) |
| Learning rate | 2e-4 |
| LR scheduler | Cosine with 100-step warmup |
| Epochs | 3 |
| Per-device batch size | 1 |
| Gradient accumulation | 16x |
| Effective batch size | 16 |
| Max sequence length | 2048 tokens |
| Optimizer | adamw_torch_fused |
| Attention | PyTorch SDPA (fused) |
| Precision | bf16 |

**Notes:** Reduced LoRA rank (8) and max sequence length (2048) for A100 40GB memory. Gradient checkpointing with `use_reentrant=False`. Max gradient norm 1.0.

## Security Coverage

### Web Security (1,625 examples)

OWASP Top 10 2021: Broken Access Control, Cryptographic Failures, Injection, Insecure Design, Security Misconfiguration, Vulnerable Components, Authentication Failures, Software Integrity Failures, Logging/Monitoring Failures, SSRF.

Languages: Python, JavaScript, Java, Go, PHP, C#, TypeScript, Ruby, Rust, Kotlin, YAML.

### AI/ML Security (747 examples)

OWASP LLM Top 10 2025: Prompt Injection, Sensitive Information Disclosure, Supply Chain Vulnerabilities, Data/Model Poisoning, Improper Output Handling, Excessive Agency, System Prompt Leakage, Vector/Embedding Weaknesses, Misinformation, Unbounded Consumption.

Frameworks: LangChain, OpenAI, Anthropic, HuggingFace, LlamaIndex, ChromaDB, Pinecone, FastAPI, Flask, vLLM, CrewAI, and 30+ more.

## SecureCode Model Collection

This model is part of the **SecureCode** collection of 9 security-specialized models:

| Model | Base | Size | Tier | HuggingFace |
|-------|------|------|------|-------------|
| Llama 3.2 SecureCode | meta-llama/Llama-3.2-3B-Instruct | 3B | Accessible | [`llama-3.2-3b-securecode`](https://huggingface.co/scthornton/llama-3.2-3b-securecode) |
| Gemma 4 E4B SecureCode | google/gemma-4-E4B-it | E4B (8B raw) | Accessible | [`gemma-4-e4b-securecode`](https://huggingface.co/scthornton/gemma-4-e4b-securecode) |
| Qwen2.5 Coder SecureCode | Qwen/Qwen2.5-Coder-7B-Instruct | 7B | Mid-size | [`qwen2.5-coder-7b-securecode`](https://huggingface.co/scthornton/qwen2.5-coder-7b-securecode) |
| DeepSeek Coder SecureCode | deepseek-ai/deepseek-coder-6.7b-instruct | 6.7B | Mid-size | [`deepseek-coder-6.7b-securecode`](https://huggingface.co/scthornton/deepseek-coder-6.7b-securecode) |
| CodeGemma SecureCode | google/codegemma-7b-it | 7B | Mid-size | [`codegemma-7b-securecode`](https://huggingface.co/scthornton/codegemma-7b-securecode) |
| CodeLlama SecureCode | codellama/CodeLlama-13b-Instruct-hf | 13B | Large | [`codellama-13b-securecode`](https://huggingface.co/scthornton/codellama-13b-securecode) |
| Qwen2.5 Coder 14B SecureCode | Qwen/Qwen2.5-Coder-14B-Instruct | 14B | Large | [`qwen2.5-coder-14b-securecode`](https://huggingface.co/scthornton/qwen2.5-coder-14b-securecode) |
| StarCoder2 SecureCode | bigcode/starcoder2-15b-instruct-v0.1 | 15B | Large | [`starcoder2-15b-securecode`](https://huggingface.co/scthornton/starcoder2-15b-securecode) |
| Granite 20B Code SecureCode | ibm-granite/granite-20b-code-instruct-8k | 20B | XL | [`granite-20b-code-securecode`](https://huggingface.co/scthornton/granite-20b-code-securecode) |

Choose based on your deployment constraints: **3B** for edge/mobile, **7B** for general use, **13B-15B** for deeper reasoning, **20B** for maximum capability.

## SecureCode Dataset Family

| Dataset | Examples | Focus | Link |
|---------|----------|-------|------|
| **SecureCode** | 2,372 | Unified (web + AI/ML) | [scthornton/securecode](https://huggingface.co/datasets/scthornton/securecode) |
| SecureCode Web | 1,625 | Web security (OWASP Top 10 2021) | [scthornton/securecode-web](https://huggingface.co/datasets/scthornton/securecode-web) |
| SecureCode AI/ML | 747 | AI/ML security (OWASP LLM Top 10 2025) | [scthornton/securecode-aiml](https://huggingface.co/datasets/scthornton/securecode-aiml) |

## Intended Use

**Use this model for:**
- Training AI coding assistants to write secure code
- Security education and training
- Vulnerability research and secure code review
- Building security-aware development tools

**Do not use this model for:**
- Offensive exploitation or automated attack generation
- Circumventing security controls
- Any activity that violates the base model's license

## Changelog

- **2026-07 (v2, current)**: Retrained on the audited SecureCode release (**2,372 examples**: 1,625 web + 747 AI/ML) using **bf16 LoRA on an NVIDIA DGX Spark GB10 (Blackwell)**. Same LoRA shape and hyperparameters as v1, except: no quantization during training, optimizer adamw_torch_fused instead of paged_adamw_8bit, PyTorch SDPA attention, dynamic per-batch padding. A new family member, [gemma-4-e4b-securecode](https://huggingface.co/scthornton/gemma-4-e4b-securecode), was added in this refresh.
- **v1 (2026-02)**: QLoRA 4-bit NF4 on NVIDIA A100 40GB, trained on the pre-audit 2,185-example dataset.

## Citation

```bibtex
@misc{thornton2026securecode,
  title={SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models},
  author={Thornton, Scott},
  year={2026},
  publisher={perfecXion.ai},
  url={https://huggingface.co/datasets/scthornton/securecode},
  note={arXiv:2512.18542}
}
```

## Links

- **Dataset**: [scthornton/securecode](https://huggingface.co/datasets/scthornton/securecode)
- **Research Paper**: [arXiv:2512.18542](https://arxiv.org/abs/2512.18542)
- **Model Collection**: [huggingface.co/collections/scthornton/securecode](https://huggingface.co/collections/scthornton/securecode)
- **Author**: [perfecXion.ai](https://perfecxion.ai)

## License

This model is released under the **apache-2.0** license (inherited from the base model). The training dataset ([SecureCode](https://huggingface.co/datasets/scthornton/securecode)) is licensed under **CC BY-NC-SA 4.0**.