README.md

---
license: gemma
base_model: google/gemma-4-E4B-it
tags:
  - security
  - cybersecurity
  - secure-coding
  - ai-security
  - owasp
  - code-generation
  - lora
  - fine-tuned
  - securecode
  - gemma-4
datasets:
  - scthornton/securecode
library_name: peft
pipeline_tag: text-generation
language:
  - code
  - en
---

# Gemma 4 E4B SecureCode

<div align="center">

![Parameters](https://img.shields.io/badge/params-E4B_(8B_raw)-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** | [Gemma 4 E4B Instruct](https://huggingface.co/google/gemma-4-E4B-it) |
| **Parameters** | E4B (~8B raw, ~4B effective via per-layer embeddings) |
| **Architecture** | Gemma 4 (multimodal base; fine-tuned and intended for text-only use) |
| **Tier** | Tier 1: Accessible |
| **Method** | bf16 LoRA (no quantization) |
| **LoRA Rank** | 16 (alpha=32) |
| **Target Modules** | `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj` on the language model decoder (the 18 KV-shared layers own no k/v weights, so k_proj/v_proj are adapted on the 24 layers that have them) |
| **Training Data** | [scthornton/securecode](https://huggingface.co/datasets/scthornton/securecode) (2,372 examples) |
| **Hardware** | NVIDIA DGX Spark GB10 (Blackwell, unified memory) |

Newest member of the SecureCode collection. Requires **transformers >= 5.13** (earlier 5.x versions have Gemma 4 training bugs; see notes below).

## Quick Start

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

# Requires transformers >= 5.13
base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-E4B-it",
    dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("scthornton/gemma-4-e4b-securecode")
model = PeftModel.from_pretrained(base_model, "scthornton/gemma-4-e4b-securecode")

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

inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, 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 (audited release):

- **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

Unlike the rest of the collection (trained with `### User:` / `### Assistant:` role markers), this model was trained through **Gemma 4's own chat template**, matching its instruction tuning.

### Hyperparameters

| Parameter | Value |
|-----------|-------|
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | language model decoder projections (see Model Details) |
| 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 | 4096 tokens |
| Optimizer | adamw_torch_fused |
| Attention | PyTorch SDPA (fused) |
| Precision | bf16 |

**Notes:** Gradient checkpointing is deliberately OFF: Gemma 4 E4B's KV-shared layers read earlier layers' KV cache entries within a single forward pass, and checkpointing force-disables that cache, silently corrupting training. transformers 5.13+ also fixes `num_items_in_batch` loss normalization for this architecture. Trained text-only; the vision and audio towers are untouched by the LoRA adapter.

## 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/E4B** for edge and resource-constrained use, **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 (v1, current)**: Initial release. Trained on the audited SecureCode release (**2,372 examples**: 1,625 web + 747 AI/ML) using **bf16 LoRA on an NVIDIA DGX Spark GB10 (Blackwell)**. Added to the SecureCode family alongside the 2026-07 refresh of the original 8 models.

## 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)
- **Paper**: [arXiv:2512.18542](https://arxiv.org/abs/2512.18542)
- **Collection**: [SecureCode models](https://huggingface.co/collections/scthornton/securecode)
- **perfecXion.ai**: [https://perfecxion.ai](https://perfecxion.ai)